Summary of Responses and Word Counts
Total WordsMean WordsTotal CharactersMean Characters
3753674.48208786414.26

1 Descriptives

1.1 Conditions

Number of Participants per Video Condition and Total
Video.ConditionsCount
0 = Negative condition170
1 = Neutral condition171
2 = Positive condition162
Total503
Eigenvalues and Factor Loadings for Socio-economic Status Principal Components Analysis. I combined Income, Education, and Occupation to create a single factor called SES.
FactorEigenvalueEducation LoadingOccupation LoadingIncome Loading
Factor 11.64985820.76827690.70250940.7523891
Factor 20.72472750.76827690.70250940.7523891
Factor 30.62541430.76827690.70250940.7523891

1.2 Descriptives Table

Combined Descriptive Statistics
nmeansdmedianminmaxrangeskewkurtosisse
Distributive Justice5032.121.302.200.0040 - 4-0.19-1.230.06
Police Effectiveness5032.261.132.330.0040 - 4-0.41-0.760.05
Legal Cynicism5032.700.852.800.0040 - 4-0.660.080.04
Expected PJ5033.141.043.500.0040 - 4-1.451.340.05
Global Procedural Justice5032.021.082.000.0040 - 4-0.09-0.810.05
Specific Procedural Justice5032.361.182.500.0040 - 4-0.16-1.200.05
Social Desirability Scale5030.560.250.590.0010 - 1-0.25-0.750.01
Self Control5032.530.762.580.2540.25 - 4-0.29-0.360.03
Normative Legitimacy5032.611.062.800.0040 - 4-0.68-0.140.05
Non-norm Legitimacy5032.770.963.000.0040 - 4-0.76-0.070.04
Male dichotomized (Male = 0)5020.530.521.000.0020 - 20.14-1.410.02
Income ($50,000-$74,999 = 2)5001.971.462.000.0040 - 40.03-1.340.07
Education (Bachelor’s degree = 2)5021.710.772.000.0030 - 30.50-1.070.03
Occupation (Unemployed = 0)5021.961.293.000.0030 - 3-0.63-1.390.06
Political Scale (Centrist = 3)3503.001.673.000.0050 - 5-0.13-1.210.09
Citizen (No = 0)5010.970.171.000.0010 - 1-5.5028.310.01
Police Family (No = 0)5030.150.350.000.0010 - 11.991.950.02
Police Contact (No = 0)5020.300.460.000.0010 - 10.85-1.290.02
Arrested5030.190.390.000.0010 - 11.570.460.02
Age50046.0415.9946.0018.008318 - 830.07-1.090.72
Race dichotomized (White = 0)4910.290.460.000.0010 - 10.91-1.180.02
Type of community (Rural = 1)5010.180.380.000.0010 - 11.660.770.02
Region of country (South = 1)5020.350.480.000.0010 - 10.62-1.610.02

1.3 Bar Graphs

2 Cronbach’s Alphas,

Boxplots, and Histograms

2.1 Distributive Justice

## 
## Reliability analysis   
## Call: alpha(x = df[, c("dist_just1", "dist_just2", "dist_just3", "dist_just4", 
##     "dist_just5")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean  sd median_r
##       0.97      0.97    0.96      0.85  28 0.0025  2.1 1.3     0.85
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.96  0.97  0.97
## Duhachek  0.96  0.97  0.97
## 
##  Reliability if an item is dropped:
##            raw_alpha std.alpha G6(smc) average_r S/N alpha se   var.r med.r
## dist_just1      0.95      0.95    0.94      0.84  20   0.0034 0.00036  0.83
## dist_just2      0.95      0.95    0.94      0.83  20   0.0034 0.00023  0.84
## dist_just3      0.96      0.96    0.95      0.85  23   0.0031 0.00112  0.85
## dist_just4      0.96      0.96    0.95      0.85  24   0.0030 0.00113  0.85
## dist_just5      0.96      0.96    0.95      0.86  24   0.0029 0.00084  0.85
## 
##  Item statistics 
##              n raw.r std.r r.cor r.drop mean  sd
## dist_just1 501  0.95  0.95  0.94   0.92  2.1 1.4
## dist_just2 502  0.95  0.95  0.95   0.93  2.0 1.4
## dist_just3 503  0.93  0.93  0.91   0.89  2.0 1.4
## dist_just4 503  0.93  0.93  0.90   0.89  2.2 1.3
## dist_just5 502  0.92  0.92  0.89   0.88  2.3 1.4
## 
## Non missing response frequency for each item
##               0    1    2    3    4 miss
## dist_just1 0.19 0.21 0.14 0.28 0.18 0.01
## dist_just2 0.21 0.20 0.13 0.30 0.17 0.00
## dist_just3 0.21 0.20 0.13 0.32 0.16 0.00
## dist_just4 0.15 0.20 0.14 0.34 0.17 0.00
## dist_just5 0.14 0.16 0.16 0.31 0.23 0.00

2.2 Police Effectiveness

## 
## Reliability analysis   
## Call: alpha(x = df[, c("pol_effect1", "pol_effect2", "pol_effect3")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean  sd median_r
##       0.91      0.91    0.87      0.77  10 0.0071  2.3 1.1     0.77
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.89  0.91  0.92
## Duhachek  0.89  0.91  0.92
## 
##  Reliability if an item is dropped:
##             raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## pol_effect1      0.87      0.87    0.77      0.77 6.5   0.0118    NA  0.77
## pol_effect2      0.85      0.85    0.74      0.74 5.7   0.0133    NA  0.74
## pol_effect3      0.89      0.89    0.80      0.80 8.1   0.0099    NA  0.80
## 
##  Item statistics 
##               n raw.r std.r r.cor r.drop mean  sd
## pol_effect1 502  0.92  0.92  0.86   0.82  2.2 1.2
## pol_effect2 503  0.93  0.93  0.88   0.84  2.4 1.2
## pol_effect3 502  0.91  0.91  0.83   0.79  2.2 1.3
## 
## Non missing response frequency for each item
##                0    1    2    3    4 miss
## pol_effect1 0.11 0.18 0.23 0.35 0.13    0
## pol_effect2 0.09 0.15 0.20 0.37 0.19    0
## pol_effect3 0.13 0.18 0.22 0.33 0.14    0

2.4 Expected PJ

## 
## Reliability analysis   
## Call: alpha(x = df[, c("expected_pj1", "expected_pj2", "expected_pj3", 
##     "expected_pj4", "expected_pj5", "expected_pj6")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean sd median_r
##       0.96      0.96    0.96      0.79  23 0.003  3.1  1      0.8
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.95  0.96  0.96
## Duhachek  0.95  0.96  0.96
## 
##  Reliability if an item is dropped:
##              raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## expected_pj1      0.94      0.95    0.94      0.78  17   0.0041 0.0029  0.79
## expected_pj2      0.95      0.95    0.94      0.78  18   0.0039 0.0043  0.79
## expected_pj3      0.95      0.95    0.94      0.79  19   0.0038 0.0036  0.80
## expected_pj4      0.96      0.96    0.95      0.82  24   0.0029 0.0011  0.82
## expected_pj5      0.95      0.95    0.94      0.79  19   0.0038 0.0054  0.82
## expected_pj6      0.95      0.95    0.95      0.79  19   0.0037 0.0036  0.80
## 
##  Item statistics 
##                n raw.r std.r r.cor r.drop mean  sd
## expected_pj1 502  0.94  0.94  0.93   0.91  3.2 1.1
## expected_pj2 500  0.92  0.92  0.91   0.89  3.2 1.1
## expected_pj3 498  0.92  0.92  0.90   0.87  3.2 1.2
## expected_pj4 503  0.85  0.85  0.81   0.78  2.9 1.2
## expected_pj5 500  0.91  0.91  0.89   0.87  3.1 1.1
## expected_pj6 502  0.91  0.91  0.89   0.87  3.3 1.1
## 
## Non missing response frequency for each item
##                 0    1    2    3    4 miss
## expected_pj1 0.04 0.07 0.06 0.26 0.57 0.00
## expected_pj2 0.04 0.07 0.10 0.26 0.53 0.01
## expected_pj3 0.06 0.07 0.07 0.21 0.59 0.01
## expected_pj4 0.06 0.11 0.11 0.32 0.40 0.00
## expected_pj5 0.05 0.07 0.09 0.33 0.45 0.01
## expected_pj6 0.04 0.06 0.06 0.23 0.61 0.00

2.5 Global Procedural Justice

## 
## Reliability analysis   
## Call: alpha(x = df[, c("global_pj1", "global_pj2", "global_pj3", "global_pj4", 
##     "global_pj5", "global_pj6")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean  sd median_r
##       0.96      0.96    0.95      0.78  22 0.0031    2 1.1     0.78
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.95  0.96  0.96
## Duhachek  0.95  0.96  0.96
## 
##  Reliability if an item is dropped:
##            raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## global_pj1      0.94      0.94    0.94      0.77  16   0.0041 0.0020  0.77
## global_pj2      0.95      0.95    0.94      0.78  18   0.0039 0.0031  0.78
## global_pj3      0.95      0.95    0.94      0.78  18   0.0038 0.0022  0.78
## global_pj4      0.95      0.95    0.94      0.79  19   0.0036 0.0030  0.79
## global_pj5      0.95      0.95    0.94      0.80  19   0.0035 0.0022  0.79
## global_pj6      0.95      0.95    0.95      0.79  19   0.0036 0.0022  0.78
## 
##  Item statistics 
##              n raw.r std.r r.cor r.drop mean  sd
## global_pj1 503  0.94  0.94  0.93   0.91  2.1 1.2
## global_pj2 499  0.91  0.91  0.90   0.87  2.1 1.2
## global_pj3 501  0.91  0.91  0.90   0.87  2.0 1.2
## global_pj4 503  0.89  0.89  0.87   0.85  1.9 1.2
## global_pj5 501  0.88  0.88  0.85   0.83  1.9 1.2
## global_pj6 503  0.89  0.89  0.86   0.84  2.2 1.2
## 
## Non missing response frequency for each item
##               0    1    2    3    4 miss
## global_pj1 0.12 0.21 0.24 0.31 0.12 0.00
## global_pj2 0.10 0.23 0.25 0.31 0.11 0.01
## global_pj3 0.12 0.25 0.27 0.26 0.10 0.01
## global_pj4 0.15 0.27 0.22 0.28 0.08 0.00
## global_pj5 0.13 0.25 0.23 0.29 0.09 0.01
## global_pj6 0.10 0.22 0.24 0.31 0.13 0.00

2.6 Specific Procedural Justice

## 
## Reliability analysis   
## Call: alpha(x = df[, c("specific_pj1", "specific_pj2", "specific_pj3", 
##     "specific_pj4", "specific_pj5", "specific_pj6")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean  sd median_r
##       0.91      0.92    0.94      0.64  11 0.0054  2.4 1.2     0.67
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt      0.9  0.91  0.92
## Duhachek   0.9  0.91  0.92
## 
##  Reliability if an item is dropped:
##              raw_alpha std.alpha G6(smc) average_r  S/N alpha se var.r med.r
## specific_pj1      0.88      0.88    0.90      0.61  7.7   0.0078 0.019  0.59
## specific_pj2      0.88      0.89    0.90      0.61  8.0   0.0076 0.019  0.64
## specific_pj3      0.91      0.91    0.92      0.66  9.6   0.0057 0.027  0.70
## specific_pj4      0.90      0.90    0.91      0.65  9.1   0.0065 0.019  0.65
## specific_pj5      0.89      0.90    0.91      0.65  9.1   0.0066 0.019  0.65
## specific_pj6      0.92      0.92    0.93      0.69 11.3   0.0054 0.016  0.70
## 
##  Item statistics 
##                n raw.r std.r r.cor r.drop mean   sd
## specific_pj1 502  0.93  0.91  0.92   0.88  2.4 1.60
## specific_pj2 501  0.91  0.90  0.90   0.86  2.3 1.65
## specific_pj3 503  0.78  0.81  0.76   0.70  3.0 1.13
## specific_pj4 502  0.86  0.83  0.81   0.77  1.6 1.54
## specific_pj5 502  0.85  0.84  0.81   0.78  1.6 1.48
## specific_pj6 503  0.69  0.74  0.67   0.61  3.3 0.91
## 
## Non missing response frequency for each item
##                 0    1    2    3    4 miss
## specific_pj1 0.22 0.13 0.09 0.18 0.39 0.00
## specific_pj2 0.26 0.11 0.09 0.18 0.36 0.01
## specific_pj3 0.05 0.06 0.16 0.30 0.43 0.00
## specific_pj4 0.37 0.20 0.12 0.11 0.19 0.00
## specific_pj5 0.34 0.15 0.23 0.11 0.17 0.00
## specific_pj6 0.02 0.04 0.11 0.33 0.51 0.00

2.7 Normative Legitimacy

## 
## Reliability analysis   
## Call: alpha(x = df[, c("norm_leg1", "norm_leg2", "norm_leg3", "norm_leg4", 
##     "norm_leg5")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean  sd median_r
##       0.92      0.92    0.91      0.69  11 0.0058  2.6 1.1      0.7
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.91  0.92  0.93
## Duhachek  0.91  0.92  0.93
## 
##  Reliability if an item is dropped:
##           raw_alpha std.alpha G6(smc) average_r  S/N alpha se  var.r med.r
## norm_leg1      0.89      0.89    0.87      0.68  8.4   0.0078 0.0014  0.69
## norm_leg2      0.90      0.90    0.87      0.69  8.8   0.0076 0.0026  0.69
## norm_leg3      0.90      0.90    0.88      0.69  9.1   0.0073 0.0047  0.69
## norm_leg4      0.89      0.89    0.87      0.68  8.5   0.0077 0.0039  0.67
## norm_leg5      0.91      0.91    0.89      0.73 10.6   0.0063 0.0012  0.71
## 
##  Item statistics 
##             n raw.r std.r r.cor r.drop mean  sd
## norm_leg1 502  0.89  0.89  0.86   0.82  2.8 1.2
## norm_leg2 501  0.88  0.88  0.84   0.80  2.6 1.2
## norm_leg3 500  0.87  0.87  0.82   0.79  2.2 1.3
## norm_leg4 502  0.88  0.89  0.86   0.82  2.8 1.1
## norm_leg5 502  0.82  0.82  0.75   0.72  2.7 1.2
## 
## Non missing response frequency for each item
##              0    1    2    3    4 miss
## norm_leg1 0.09 0.08 0.15 0.34 0.34 0.00
## norm_leg2 0.09 0.12 0.17 0.36 0.27 0.01
## norm_leg3 0.12 0.19 0.21 0.29 0.19 0.01
## norm_leg4 0.06 0.08 0.18 0.38 0.30 0.00
## norm_leg5 0.07 0.08 0.21 0.35 0.28 0.00

2.8 Non-normative Legitimacy

## 
## Reliability analysis   
## Call: alpha(x = df[, c("nonnorm_leg1", "nonnorm_leg2", "nonnorm_leg3", 
##     "nonnorm_leg4", "nonnorm_leg5")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N  ase mean   sd median_r
##       0.85      0.85    0.83      0.53 5.7 0.01  2.8 0.96     0.52
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.83  0.85  0.87
## Duhachek  0.83  0.85  0.87
## 
##  Reliability if an item is dropped:
##              raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## nonnorm_leg1      0.84      0.84    0.80      0.57 5.2    0.011 0.0051  0.59
## nonnorm_leg2      0.83      0.84    0.80      0.56 5.1    0.012 0.0057  0.58
## nonnorm_leg3      0.81      0.81    0.78      0.52 4.4    0.013 0.0057  0.52
## nonnorm_leg4      0.81      0.81    0.78      0.52 4.4    0.013 0.0066  0.51
## nonnorm_leg5      0.79      0.79    0.75      0.49 3.8    0.015 0.0036  0.47
## 
##  Item statistics 
##                n raw.r std.r r.cor r.drop mean  sd
## nonnorm_leg1 503  0.74  0.74  0.63   0.58  2.8 1.2
## nonnorm_leg2 502  0.72  0.75  0.65   0.60  3.1 1.0
## nonnorm_leg3 503  0.82  0.80  0.74   0.68  2.2 1.4
## nonnorm_leg4 503  0.82  0.81  0.74   0.69  2.8 1.3
## nonnorm_leg5 502  0.86  0.86  0.83   0.76  3.0 1.2
## 
## Non missing response frequency for each item
##                 0    1    2    3    4 miss
## nonnorm_leg1 0.07 0.10 0.17 0.31 0.35    0
## nonnorm_leg2 0.02 0.07 0.12 0.36 0.44    0
## nonnorm_leg3 0.14 0.22 0.14 0.28 0.22    0
## nonnorm_leg4 0.09 0.13 0.09 0.32 0.38    0
## nonnorm_leg5 0.06 0.10 0.08 0.36 0.40    0

2.9 Difference Scores

This is the difference between expected procedural justice and specific procedural justice.

## 
## Reliability analysis   
## Call: alpha(x = df[, c("diff1", "diff2", "diff3", "diff4", "diff5", 
##     "diff6")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase  mean  sd median_r
##       0.92      0.92    0.93      0.67  12 0.005 -0.79 1.4      0.7
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.91  0.92  0.93
## Duhachek  0.91  0.92  0.93
## 
##  Reliability if an item is dropped:
##       raw_alpha std.alpha G6(smc) average_r  S/N alpha se  var.r med.r
## diff1      0.90      0.90    0.90      0.64  9.0   0.0069 0.0094  0.64
## diff2      0.90      0.90    0.90      0.65  9.2   0.0068 0.0095  0.66
## diff3      0.91      0.91    0.92      0.68 10.6   0.0055 0.0154  0.70
## diff4      0.91      0.91    0.92      0.68 10.7   0.0058 0.0111  0.70
## diff5      0.91      0.91    0.91      0.67 10.3   0.0060 0.0121  0.69
## diff6      0.93      0.93    0.93      0.71 12.4   0.0051 0.0086  0.71
## 
##  Item statistics 
##         n raw.r std.r r.cor r.drop   mean  sd
## diff1 501  0.92  0.91  0.91   0.88 -0.848 1.8
## diff2 498  0.91  0.90  0.90   0.86 -0.908 1.9
## diff3 498  0.82  0.84  0.80   0.75 -0.199 1.4
## diff4 502  0.85  0.84  0.80   0.77 -1.335 1.8
## diff5 499  0.86  0.85  0.82   0.79 -1.429 1.7
## diff6 502  0.74  0.77  0.71   0.67 -0.018 1.2
## 
## Non missing response frequency for each item
##         -4   -3   -2   -1    0    1    2    3    4 miss
## diff1 0.13 0.10 0.07 0.16 0.34 0.13 0.04 0.02 0.01 0.01
## diff2 0.14 0.11 0.10 0.14 0.30 0.11 0.06 0.03 0.00 0.01
## diff3 0.03 0.03 0.08 0.19 0.44 0.12 0.07 0.02 0.01 0.01
## diff4 0.15 0.16 0.16 0.17 0.23 0.08 0.02 0.03 0.00 0.00
## diff5 0.17 0.12 0.18 0.17 0.22 0.08 0.03 0.01 0.00 0.01
## diff6 0.01 0.02 0.04 0.21 0.49 0.14 0.06 0.03 0.01 0.00

2.10 Brief Self Control

## 
## Reliability analysis   
## Call: alpha(x = df[, c("BSC1", "BSC2R", "BSC3R", "BSC4R", "BSC5R", 
##     "BSC6", "BSC7R", "BSC8", "BSC9R", "BSC10R", "BSC11", "BSC12R", 
##     "BSC13R")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
##        0.9       0.9    0.91       0.4 8.8 0.0068  2.6 0.75     0.41
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.88   0.9  0.91
## Duhachek  0.88   0.9  0.91
## 
##  Reliability if an item is dropped:
##        raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## BSC1        0.89      0.89    0.89      0.40 7.9   0.0075 0.0091  0.41
## BSC2R       0.88      0.89    0.90      0.39 7.8   0.0076 0.0109  0.40
## BSC3R       0.89      0.89    0.90      0.40 8.0   0.0075 0.0111  0.41
## BSC4R       0.89      0.90    0.90      0.42 8.6   0.0070 0.0085  0.42
## BSC5R       0.89      0.89    0.90      0.40 7.9   0.0075 0.0106  0.41
## BSC6        0.89      0.89    0.90      0.41 8.4   0.0072 0.0096  0.41
## BSC7R       0.89      0.89    0.90      0.40 8.1   0.0073 0.0109  0.41
## BSC8        0.89      0.89    0.90      0.41 8.4   0.0071 0.0079  0.42
## BSC9R       0.89      0.89    0.90      0.40 7.9   0.0075 0.0106  0.41
## BSC10R      0.89      0.89    0.90      0.41 8.3   0.0072 0.0114  0.42
## BSC11       0.89      0.89    0.90      0.41 8.2   0.0073 0.0110  0.41
## BSC12R      0.89      0.89    0.90      0.40 7.9   0.0074 0.0105  0.40
## BSC13R      0.89      0.89    0.90      0.41 8.2   0.0073 0.0106  0.41
## 
##  Item statistics 
##          n raw.r std.r r.cor r.drop mean   sd
## BSC1   503  0.73  0.72  0.71   0.67  2.2 1.13
## BSC2R  501  0.74  0.74  0.71   0.68  2.4 1.16
## BSC3R  503  0.70  0.70  0.67   0.64  3.0 1.06
## BSC4R  500  0.55  0.56  0.51   0.47  2.9 1.06
## BSC5R  501  0.71  0.72  0.70   0.65  2.7 1.04
## BSC6   501  0.62  0.62  0.59   0.54  2.2 1.14
## BSC7R  501  0.68  0.67  0.63   0.60  2.2 1.31
## BSC8   503  0.62  0.60  0.58   0.53  1.7 1.24
## BSC9R  502  0.71  0.72  0.69   0.65  2.7 1.11
## BSC10R 501  0.64  0.64  0.59   0.56  2.8 1.20
## BSC11  502  0.66  0.66  0.62   0.59  2.7 1.07
## BSC12R 502  0.70  0.71  0.69   0.64  3.0 0.99
## BSC13R 503  0.64  0.65  0.62   0.57  3.1 1.00
## 
## Non missing response frequency for each item
##           0    1    2    3    4 miss
## BSC1   0.05 0.25 0.28 0.27 0.15 0.00
## BSC2R  0.07 0.17 0.23 0.36 0.17 0.01
## BSC3R  0.04 0.07 0.15 0.38 0.37 0.00
## BSC4R  0.04 0.07 0.17 0.42 0.30 0.01
## BSC5R  0.04 0.07 0.23 0.42 0.23 0.01
## BSC6   0.06 0.25 0.26 0.30 0.13 0.01
## BSC7R  0.15 0.17 0.22 0.29 0.17 0.01
## BSC8   0.21 0.25 0.28 0.17 0.10 0.00
## BSC9R  0.04 0.11 0.22 0.36 0.27 0.00
## BSC10R 0.06 0.11 0.18 0.31 0.34 0.01
## BSC11  0.03 0.11 0.26 0.34 0.26 0.00
## BSC12R 0.02 0.07 0.16 0.37 0.38 0.00
## BSC13R 0.03 0.06 0.14 0.37 0.41 0.00

2.11 Social Desirability

## 
## Reliability analysis   
## Call: alpha(x = df[, c("SDS1R", "SDS2", "SDS3", "SDS4R", "SDS5", "SDS6R", 
##     "SDS7R", "SDS8", "SDS9", "SDS10", "SDS11R", "SDS12", "SDS13", 
##     "SDS14", "SDS15R", "SDS16", "SDS17R")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N  ase mean   sd median_r
##       0.84      0.84    0.84      0.23 5.1 0.01 0.56 0.25     0.24
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.82  0.84  0.86
## Duhachek  0.82  0.84  0.86
## 
##  Reliability if an item is dropped:
##        raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## SDS1R       0.84      0.84    0.84      0.24 5.1    0.011 0.0071  0.25
## SDS2        0.82      0.82    0.83      0.23 4.7    0.011 0.0072  0.23
## SDS3        0.83      0.83    0.83      0.23 4.8    0.011 0.0078  0.24
## SDS4R       0.84      0.84    0.84      0.24 5.2    0.010 0.0067  0.25
## SDS5        0.83      0.83    0.83      0.23 4.8    0.011 0.0074  0.24
## SDS6R       0.83      0.82    0.83      0.23 4.7    0.011 0.0074  0.24
## SDS7R       0.83      0.82    0.83      0.23 4.7    0.011 0.0078  0.23
## SDS8        0.83      0.82    0.83      0.23 4.7    0.011 0.0078  0.23
## SDS9        0.83      0.83    0.83      0.23 4.8    0.011 0.0078  0.24
## SDS10       0.82      0.82    0.83      0.22 4.6    0.011 0.0078  0.23
## SDS11R      0.83      0.82    0.83      0.23 4.7    0.011 0.0080  0.23
## SDS12       0.83      0.83    0.84      0.24 4.9    0.011 0.0077  0.24
## SDS13       0.82      0.82    0.82      0.22 4.6    0.012 0.0068  0.23
## SDS14       0.82      0.82    0.83      0.23 4.6    0.011 0.0070  0.23
## SDS15R      0.83      0.83    0.84      0.23 4.9    0.011 0.0079  0.24
## SDS16       0.84      0.83    0.84      0.24 5.1    0.011 0.0072  0.25
## SDS17R      0.83      0.82    0.83      0.23 4.7    0.011 0.0077  0.23
## 
##  Item statistics 
##          n raw.r std.r r.cor r.drop mean   sd
## SDS1R  502  0.37  0.38  0.31   0.28 0.75 0.43
## SDS2   503  0.59  0.59  0.56   0.51 0.66 0.47
## SDS3   502  0.53  0.53  0.49   0.44 0.66 0.48
## SDS4R  503  0.36  0.35  0.27   0.25 0.45 0.50
## SDS5   503  0.53  0.52  0.48   0.43 0.57 0.50
## SDS6R  503  0.58  0.58  0.54   0.49 0.47 0.50
## SDS7R  499  0.57  0.57  0.53   0.49 0.43 0.50
## SDS8   502  0.57  0.57  0.54   0.49 0.66 0.48
## SDS9   503  0.51  0.51  0.46   0.42 0.68 0.47
## SDS10  503  0.60  0.60  0.57   0.52 0.71 0.45
## SDS11R 503  0.58  0.58  0.54   0.50 0.40 0.49
## SDS12  502  0.46  0.46  0.40   0.37 0.69 0.46
## SDS13  502  0.65  0.65  0.63   0.58 0.57 0.50
## SDS14  503  0.60  0.60  0.57   0.52 0.54 0.50
## SDS15R 503  0.47  0.47  0.42   0.38 0.37 0.48
## SDS16  503  0.38  0.40  0.33   0.30 0.17 0.38
## SDS17R 503  0.58  0.58  0.54   0.50 0.67 0.47
## 
## Non missing response frequency for each item
##           0    1 miss
## SDS1R  0.25 0.75 0.00
## SDS2   0.34 0.66 0.00
## SDS3   0.34 0.66 0.00
## SDS4R  0.55 0.45 0.00
## SDS5   0.43 0.57 0.00
## SDS6R  0.53 0.47 0.00
## SDS7R  0.57 0.43 0.01
## SDS8   0.34 0.66 0.00
## SDS9   0.32 0.68 0.00
## SDS10  0.29 0.71 0.00
## SDS11R 0.60 0.40 0.00
## SDS12  0.31 0.69 0.00
## SDS13  0.43 0.57 0.00
## SDS14  0.46 0.54 0.00
## SDS15R 0.63 0.37 0.00
## SDS16  0.83 0.17 0.00
## SDS17R 0.33 0.67 0.00

3 Correlations

Correlation Matrix with Significance Indicators
Distributive JusticePolice EffectivenessLegal CynicismExpected Procedural JusticeGlobal Procedural JusticeSpecific Procedural JusticeSocial Desirability ScaleNormative LegitimacyNon-norm Legitimacy
Distributive Justice1.000.76*-0.60*0.39*0.74*0.16*0.24*0.54*-0.35*
Police Effectiveness0.76*1.00-0.54*0.37*0.73*0.15*0.21*0.50*-0.36*
Legal Cynicism-0.60*-0.54*1.00-0.26*-0.63*-0.21*-0.30*-0.43*0.38*
Expected Procedural Justice0.39*0.37*-0.26*1.000.42*0.18*0.19*0.42*-0.24*
Global Procedural Justice0.74*0.73*-0.63*0.42*1.000.18*0.19*0.56*-0.40*
Specific Procedural Justice0.16*0.15*-0.21*0.18*0.18*1.000.13*0.24*-0.30*
Social Desirability Scale0.24*0.21*-0.30*0.19*0.19*0.13*1.000.25*-0.20*
Normative Legitimacy0.54*0.50*-0.43*0.42*0.56*0.24*0.25*1.00-0.23*
Non-norm Legitimacy-0.35*-0.36*0.38*-0.24*-0.40*-0.30*-0.20*-0.23*1.00

Correlation Matrix with Significance Indicators (Diff Scores, Normative Legitimacy, Non-norm Legitimacy)
Difference ScoresNormative LegitimacyNon-norm Legitimacy
Difference Scores1.000.11*0.07
Normative Legitimacy0.11*1.00-0.23*
Non-norm Legitimacy0.07-0.23*1.00

3.1 Scatterplots

4 Qualitative Data

## 
## Call:
## lm(formula = norm_leg ~ video_condition + global_pj + expected_pj + 
##     dist_just + pol_effect + legal_cyn + open_quest + open_quest_count_words, 
##     data = df_temp)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3025 -0.5255  0.0422  0.5207  2.0361 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             1.1918188  0.2677850   4.451 1.07e-05 ***
## video_condition1        0.2640244  0.0943499   2.798 0.005349 ** 
## video_condition2        0.2316724  0.0947635   2.445 0.014865 *  
## global_pj               0.2246129  0.0611420   3.674 0.000267 ***
## expected_pj             0.1932743  0.0413919   4.669 3.96e-06 ***
## dist_just               0.1930098  0.0538442   3.585 0.000373 ***
## pol_effect              0.0262491  0.0584421   0.449 0.653534    
## legal_cyn              -0.0869702  0.0605507  -1.436 0.151582    
## open_quest              0.0005127  0.0012565   0.408 0.683408    
## open_quest_count_words -0.0033612  0.0070390  -0.478 0.633224    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8329 on 466 degrees of freedom
## Multiple R-squared:  0.3894, Adjusted R-squared:  0.3776 
## F-statistic: 33.02 on 9 and 466 DF,  p-value: < 2.2e-16
## 
## Call:
## lm(formula = norm_leg ~ video_condition + global_pj + expected_pj + 
##     dist_just + pol_effect + legal_cyn + open_quest + open_quest_count_words + 
##     BSC + SDS + region_split + community_split + race_split + 
##     age + arrested + pol_contact + pol_fam + citizen + SES + 
##     Male_split, data = df_temp)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3274 -0.4967  0.0616  0.5062  2.1203 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.4240172  0.4147630   1.022  0.30718    
## video_condition1        0.2530985  0.0955393   2.649  0.00835 ** 
## video_condition2        0.2285719  0.0966679   2.365  0.01847 *  
## global_pj               0.2597262  0.0619922   4.190 3.36e-05 ***
## expected_pj             0.1804052  0.0423043   4.264 2.44e-05 ***
## dist_just               0.1781332  0.0557886   3.193  0.00151 ** 
## pol_effect              0.0285342  0.0588374   0.485  0.62793    
## legal_cyn              -0.0429114  0.0629648  -0.682  0.49589    
## open_quest              0.0006344  0.0012610   0.503  0.61518    
## open_quest_count_words -0.0040677  0.0070754  -0.575  0.56563    
## BSC                     0.0196509  0.0633885   0.310  0.75670    
## SDS                     0.4214778  0.1830975   2.302  0.02179 *  
## region_split            0.0448371  0.0811383   0.553  0.58081    
## community_split         0.0129281  0.1022360   0.126  0.89943    
## race_split              0.0143609  0.0876965   0.164  0.87000    
## age                    -0.0017978  0.0027798  -0.647  0.51812    
## arrested                0.0539909  0.1016583   0.531  0.59561    
## pol_contact            -0.0492459  0.0850106  -0.579  0.56268    
## pol_fam                 0.0630640  0.1131925   0.557  0.57771    
## citizen                 0.3273103  0.2477124   1.321  0.18706    
## SES                    -0.0067059  0.0400689  -0.167  0.86716    
## Male_split              0.2050022  0.0778363   2.634  0.00873 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8287 on 454 degrees of freedom
## Multiple R-squared:  0.4112, Adjusted R-squared:  0.3839 
## F-statistic:  15.1 on 21 and 454 DF,  p-value: < 2.2e-16
## Analysis of Variance Table
## 
## Model 1: norm_leg ~ video_condition + global_pj + expected_pj + dist_just + 
##     pol_effect + legal_cyn + open_quest + open_quest_count_words
## Model 2: norm_leg ~ video_condition + global_pj + expected_pj + dist_just + 
##     pol_effect + legal_cyn + open_quest + open_quest_count_words + 
##     BSC + SDS + region_split + community_split + race_split + 
##     age + arrested + pol_contact + pol_fam + citizen + SES + 
##     Male_split
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    466 323.29                           
## 2    454 311.77 12    11.527 1.3988 0.1627

5 Regressions

5.1 Legitimacy is predicted by Video Condition, Global PJ, Expected PJ, and Controls

## 
## Call:
## lm(formula = norm_leg ~ video_condition + global_pj + expected_pj + 
##     dist_just + pol_effect + legal_cyn + BSC + SDS + region_split + 
##     community_split + race_split + age + arrested + pol_contact + 
##     pol_fam + citizen + SES + Male_split, data = df_temp)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3231 -0.4859  0.0636  0.4985  2.1292 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.392687   0.412129   0.953  0.34118    
## video_condition1  0.251563   0.095263   2.641  0.00856 ** 
## video_condition2  0.230762   0.096122   2.401  0.01676 *  
## global_pj         0.260874   0.061875   4.216 3.00e-05 ***
## expected_pj       0.178316   0.042095   4.236 2.75e-05 ***
## dist_just         0.171639   0.055175   3.111  0.00198 ** 
## pol_effect        0.031607   0.058614   0.539  0.58998    
## legal_cyn        -0.049864   0.062294  -0.800  0.42386    
## BSC               0.018546   0.063282   0.293  0.76960    
## SDS               0.426654   0.182699   2.335  0.01996 *  
## region_split      0.048864   0.080870   0.604  0.54599    
## community_split   0.016364   0.102013   0.160  0.87263    
## race_split        0.015553   0.087450   0.178  0.85892    
## age              -0.001388   0.002733  -0.508  0.61181    
## arrested          0.053325   0.101464   0.526  0.59945    
## pol_contact      -0.044617   0.084663  -0.527  0.59846    
## pol_fam           0.065639   0.112789   0.582  0.56088    
## citizen           0.326311   0.247363   1.319  0.18778    
## SES              -0.006859   0.039995  -0.171  0.86391    
## Male_split        0.201546   0.077580   2.598  0.00968 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8275 on 456 degrees of freedom
## Multiple R-squared:  0.4102, Adjusted R-squared:  0.3857 
## F-statistic: 16.69 on 19 and 456 DF,  p-value: < 2.2e-16

5.2 Legitimacy is predicted by Video Condition, Global PJ, Expected PJ, VC*GPJ, and Controls

## 
## Call:
## lm(formula = norm_leg ~ video_condition + global_pj_cent + expected_pj + 
##     video_condition * global_pj_cent + dist_just + pol_effect + 
##     legal_cyn + BSC + SDS + region_split + community_split + 
##     race_split + age + arrested + pol_contact + pol_fam + citizen + 
##     SES + Male_split, data = df_temp)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3184 -0.5021  0.0524  0.4956  2.1194 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      0.931574   0.405601   2.297  0.02209 *  
## video_condition1                 0.253016   0.095533   2.648  0.00837 ** 
## video_condition2                 0.228590   0.096302   2.374  0.01803 *  
## global_pj_cent                   0.305892   0.077831   3.930 9.81e-05 ***
## expected_pj                      0.174254   0.042383   4.111 4.67e-05 ***
## dist_just                        0.170913   0.055241   3.094  0.00210 ** 
## pol_effect                       0.034371   0.058874   0.584  0.55964    
## legal_cyn                       -0.055936   0.062887  -0.889  0.37422    
## BSC                              0.016081   0.063427   0.254  0.79997    
## SDS                              0.430983   0.182966   2.356  0.01892 *  
## region_split                     0.043293   0.081155   0.533  0.59398    
## community_split                  0.012769   0.102275   0.125  0.90070    
## race_split                       0.010297   0.087740   0.117  0.90663    
## age                             -0.001109   0.002756  -0.402  0.68763    
## arrested                         0.052945   0.101577   0.521  0.60246    
## pol_contact                     -0.036345   0.085164  -0.427  0.66975    
## pol_fam                          0.058959   0.113545   0.519  0.60383    
## citizen                          0.329171   0.248036   1.327  0.18514    
## SES                             -0.010025   0.040172  -0.250  0.80306    
## Male_split                       0.206220   0.077813   2.650  0.00833 ** 
## video_condition1:global_pj_cent -0.062106   0.088837  -0.699  0.48485    
## video_condition2:global_pj_cent -0.084950   0.087981  -0.966  0.33478    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8284 on 454 degrees of freedom
## Multiple R-squared:  0.4115, Adjusted R-squared:  0.3843 
## F-statistic: 15.12 on 21 and 454 DF,  p-value: < 2.2e-16

5.3 Specific PJ is predicted by Video Condition, Global PJ, Expected PJ, VC*GPJ, and Controls

## 
## Call:
## lm(formula = specific_pj ~ video_condition + global_pj_cent + 
##     expected_pj + video_condition * global_pj_cent + dist_just + 
##     pol_effect + legal_cyn + BSC + SDS + region_split + community_split + 
##     race_split + age + arrested + pol_contact + pol_fam + citizen + 
##     SES + Male_split, data = df_new)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.19102 -0.39413  0.02711  0.37327  2.45579 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      1.235979   0.318739   3.878 0.000121 ***
## video_condition1                 1.178087   0.075074  15.692  < 2e-16 ***
## video_condition2                 2.277925   0.075678  30.100  < 2e-16 ***
## global_pj_cent                   0.107590   0.061163   1.759 0.079239 .  
## expected_pj                      0.115728   0.033306   3.475 0.000561 ***
## dist_just                        0.005667   0.043411   0.131 0.896203    
## pol_effect                       0.025298   0.046266   0.547 0.584785    
## legal_cyn                       -0.085350   0.049419  -1.727 0.084838 .  
## BSC                              0.029650   0.049843   0.595 0.552227    
## SDS                             -0.042791   0.143782  -0.298 0.766138    
## region_split                    -0.084298   0.063775  -1.322 0.186901    
## community_split                 -0.035990   0.080372  -0.448 0.654511    
## race_split                       0.008293   0.068950   0.120 0.904320    
## age                             -0.003286   0.002166  -1.517 0.129860    
## arrested                         0.111610   0.079823   1.398 0.162731    
## pol_contact                     -0.008184   0.066925  -0.122 0.902727    
## pol_fam                         -0.085232   0.089228  -0.955 0.339979    
## citizen                         -0.132609   0.194917  -0.680 0.496639    
## SES                              0.021137   0.031569   0.670 0.503478    
## Male_split                       0.075695   0.061149   1.238 0.216401    
## video_condition1:global_pj_cent  0.120503   0.069812   1.726 0.085009 .  
## video_condition2:global_pj_cent -0.051740   0.069139  -0.748 0.454635    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.651 on 454 degrees of freedom
## Multiple R-squared:  0.7071, Adjusted R-squared:  0.6936 
## F-statistic:  52.2 on 21 and 454 DF,  p-value: < 2.2e-16

5.4 Non-normative Legitibacy is predicted by Video Condition, Global PJ, Expected PJ, VC*GPJ, and Controls

## 
## Call:
## lm(formula = nonnorm_leg ~ video_condition + global_pj_cent + 
##     expected_pj + video_condition * global_pj_cent + dist_just + 
##     pol_effect + legal_cyn + SDS + BSC + age + region_split + 
##     community_split + race_split + arrested + pol_contact + pol_fam + 
##     citizen + SES + Male_split, data = df_new)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6859 -0.4894  0.1001  0.5375  2.2146 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      3.764368   0.405266   9.289  < 2e-16 ***
## video_condition1                -0.108064   0.095454  -1.132  0.25819    
## video_condition2                -0.495975   0.096223  -5.154  3.8e-07 ***
## global_pj_cent                  -0.192371   0.077767  -2.474  0.01374 *  
## expected_pj                     -0.031474   0.042348  -0.743  0.45773    
## dist_just                        0.094993   0.055196   1.721  0.08593 .  
## pol_effect                      -0.126131   0.058826  -2.144  0.03255 *  
## legal_cyn                        0.189406   0.062835   3.014  0.00272 ** 
## SDS                             -0.065537   0.182814  -0.358  0.72014    
## BSC                             -0.220548   0.063374  -3.480  0.00055 ***
## age                             -0.007669   0.002754  -2.785  0.00558 ** 
## region_split                    -0.047249   0.081088  -0.583  0.56039    
## community_split                  0.084473   0.102190   0.827  0.40888    
## race_split                       0.160764   0.087668   1.834  0.06734 .  
## arrested                         0.097598   0.101493   0.962  0.33675    
## pol_contact                     -0.072336   0.085093  -0.850  0.39573    
## pol_fam                         -0.080384   0.113451  -0.709  0.47898    
## citizen                         -0.247774   0.247831  -1.000  0.31795    
## SES                              0.077008   0.040139   1.919  0.05567 .  
## Male_split                       0.082355   0.077748   1.059  0.29005    
## video_condition1:global_pj_cent  0.106778   0.088764   1.203  0.22962    
## video_condition2:global_pj_cent  0.014126   0.087908   0.161  0.87241    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8277 on 454 degrees of freedom
## Multiple R-squared:  0.2964, Adjusted R-squared:  0.2639 
## F-statistic: 9.108 on 21 and 454 DF,  p-value: < 2.2e-16

5.5 Legitimacy is predicted by Video Condition, Global PJ, Expected PJ, VC*ExPJ, and Controls

## 
## Call:
## lm(formula = norm_leg ~ video_condition + global_pj + expected_pj_cent + 
##     video_condition * expected_pj_cent + dist_just + pol_effect + 
##     legal_cyn + SDS + BSC + age + region_split + community_split + 
##     race_split + arrested + pol_contact + pol_fam + citizen + 
##     SES + Male_split, data = df_new)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2949 -0.4949  0.0602  0.5027  2.1495 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        0.977019   0.415826   2.350 0.019222 *  
## video_condition1                   0.255166   0.095629   2.668 0.007896 ** 
## video_condition2                   0.229073   0.096266   2.380 0.017744 *  
## global_pj                          0.256553   0.062204   4.124 4.42e-05 ***
## expected_pj_cent                   0.219995   0.064411   3.415 0.000694 ***
## dist_just                          0.169293   0.055474   3.052 0.002408 ** 
## pol_effect                         0.031740   0.058693   0.541 0.588927    
## legal_cyn                         -0.056521   0.062969  -0.898 0.369880    
## SDS                                0.432571   0.183062   2.363 0.018549 *  
## BSC                                0.017935   0.063830   0.281 0.778846    
## age                               -0.001221   0.002743  -0.445 0.656546    
## region_split                       0.046861   0.081489   0.575 0.565533    
## community_split                    0.017566   0.102169   0.172 0.863569    
## race_split                         0.007304   0.088097   0.083 0.933959    
## arrested                           0.048818   0.101720   0.480 0.631514    
## pol_contact                       -0.042589   0.085212  -0.500 0.617459    
## pol_fam                            0.067553   0.112969   0.598 0.550150    
## citizen                            0.326116   0.247685   1.317 0.188619    
## SES                               -0.010288   0.040297  -0.255 0.798599    
## Male_split                         0.204278   0.077790   2.626 0.008931 ** 
## video_condition1:expected_pj_cent -0.081897   0.093549  -0.875 0.381791    
## video_condition2:expected_pj_cent -0.052657   0.090941  -0.579 0.562862    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8286 on 454 degrees of freedom
## Multiple R-squared:  0.4113, Adjusted R-squared:  0.3841 
## F-statistic:  15.1 on 21 and 454 DF,  p-value: < 2.2e-16

5.6 Specific PJ is predicted by Video Condition, Global PJ, Expected PJ, VC*ExPJ, and Controls

## 
## Call:
## lm(formula = specific_pj ~ video_condition + global_pj + expected_pj_cent + 
##     video_condition * expected_pj_cent + dist_just + pol_effect + 
##     legal_cyn + SDS + BSC + age + region_split + community_split + 
##     race_split + arrested + pol_contact + pol_fam + citizen + 
##     SES + Male_split, data = df_new)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.28252 -0.42840  0.00699  0.36869  2.39533 
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        1.2661676  0.3257163   3.887 0.000116 ***
## video_condition1                   1.1796221  0.0749058  15.748  < 2e-16 ***
## video_condition2                   2.2887881  0.0754050  30.353  < 2e-16 ***
## global_pj                          0.1414997  0.0487244   2.904 0.003863 ** 
## expected_pj_cent                   0.0007816  0.0504532   0.015 0.987646    
## dist_just                          0.0113963  0.0434525   0.262 0.793232    
## pol_effect                         0.0168763  0.0459741   0.367 0.713728    
## legal_cyn                         -0.0780987  0.0493238  -1.583 0.114030    
## SDS                               -0.0633596  0.1433919  -0.442 0.658799    
## BSC                                0.0346824  0.0499978   0.694 0.488238    
## age                               -0.0034118  0.0021489  -1.588 0.113059    
## region_split                      -0.0791152  0.0638300  -1.239 0.215813    
## community_split                   -0.0305922  0.0800290  -0.382 0.702444    
## race_split                         0.0255444  0.0690060   0.370 0.711424    
## arrested                           0.1231090  0.0796773   1.545 0.123020    
## pol_contact                       -0.0142083  0.0667468  -0.213 0.831525    
## pol_fam                           -0.0697315  0.0884884  -0.788 0.431091    
## citizen                           -0.1047815  0.1940116  -0.540 0.589408    
## SES                                0.0321699  0.0315642   1.019 0.308656    
## Male_split                         0.0700888  0.0609332   1.150 0.250644    
## video_condition1:expected_pj_cent  0.2154642  0.0732766   2.940 0.003445 ** 
## video_condition2:expected_pj_cent  0.1442486  0.0712339   2.025 0.043452 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.649 on 454 degrees of freedom
## Multiple R-squared:  0.7089, Adjusted R-squared:  0.6954 
## F-statistic: 52.65 on 21 and 454 DF,  p-value: < 2.2e-16

5.7 Non-normative Legitimacy is predicted by Video Condition, Global PJ, Expected PJ, VC*ExPJ, and Controls

## 
## Call:
## lm(formula = nonnorm_leg ~ video_condition + global_pj + expected_pj_cent + 
##     video_condition * expected_pj_cent + dist_just + pol_effect + 
##     legal_cyn + SDS + BSC + age + region_split + community_split + 
##     race_split + arrested + pol_contact + pol_fam + citizen + 
##     SES + Male_split, data = df_new)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7654 -0.4775  0.1097  0.5612  2.1563 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        3.954077   0.415924   9.507  < 2e-16 ***
## video_condition1                  -0.104293   0.095651  -1.090 0.276139    
## video_condition2                  -0.493710   0.096289  -5.127 4.36e-07 ***
## global_pj                         -0.157355   0.062219  -2.529 0.011775 *  
## expected_pj_cent                  -0.018064   0.064426  -0.280 0.779311    
## dist_just                          0.097299   0.055487   1.754 0.080183 .  
## pol_effect                        -0.129984   0.058707  -2.214 0.027316 *  
## legal_cyn                          0.182314   0.062984   2.895 0.003979 ** 
## SDS                               -0.064063   0.183105  -0.350 0.726598    
## BSC                               -0.213886   0.063845  -3.350 0.000875 ***
## age                               -0.007326   0.002744  -2.670 0.007866 ** 
## region_split                      -0.057551   0.081508  -0.706 0.480503    
## community_split                    0.089306   0.102193   0.874 0.382637    
## race_split                         0.156348   0.088117   1.774 0.076682 .  
## arrested                           0.096866   0.101744   0.952 0.341576    
## pol_contact                       -0.061458   0.085232  -0.721 0.471243    
## pol_fam                           -0.072470   0.112996  -0.641 0.521619    
## citizen                           -0.229892   0.247744  -0.928 0.353930    
## SES                                0.074131   0.040306   1.839 0.066537 .  
## Male_split                         0.084077   0.077809   1.081 0.280468    
## video_condition1:expected_pj_cent  0.004317   0.093571   0.046 0.963224    
## video_condition2:expected_pj_cent -0.059649   0.090962  -0.656 0.512311    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8288 on 454 degrees of freedom
## Multiple R-squared:  0.2946, Adjusted R-squared:  0.262 
## F-statistic:  9.03 on 21 and 454 DF,  p-value: < 2.2e-16

6 Path Analyses

6.1 Path Analysis 1 - Normative Legitimacy with Contrls: Male, Region, SDS

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## This is lavaan 0.6-17
## lavaan is FREE software! Please report any bugs.
## 
## 
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## 
## 
## The following object is masked from 'package:psych':
## 
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## 
## 
## Loading required package: OpenMx
## 
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## 
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## 
##     %&%, expm
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## 
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## 
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## 
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##   method          from  
##   predict.MxModel OpenMx
## 
## 
## Attaching package: 'kableExtra'
## 
## 
## The following object is masked from 'package:dplyr':
## 
##     group_rows
## lavaan 0.6.17 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        18
## 
##   Number of observations                           501
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 3.119
##   Degrees of freedom                                 5
##   P-value (Chi-square)                           0.682
## 
## Model Test Baseline Model:
## 
##   Test statistic                               871.105
##   Degrees of freedom                                21
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.009
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -1100.028
##   Loglikelihood unrestricted model (H1)      -1098.469
##                                                       
##   Akaike (AIC)                                2236.057
##   Bayesian (BIC)                              2311.956
##   Sample-size adjusted Bayesian (SABIC)       2254.822
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.048
##   P-value H_0: RMSEA <= 0.050                    0.957
##   P-value H_0: RMSEA >= 0.080                    0.001
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.005
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   NL ~                                                                  
##     VC1       (c1)    0.064    0.113    0.568    0.570    0.064    0.029
##     VC2       (c2)   -0.071    0.160   -0.441    0.659   -0.071   -0.031
##     PE                0.068    0.054    1.251    0.211    0.068    0.072
##     DJ                0.137    0.050    2.740    0.006    0.137    0.167
##     LC               -0.048    0.059   -0.817    0.414   -0.048   -0.039
##     ExPJ              0.172    0.040    4.324    0.000    0.172    0.169
##     GPJ               0.248    0.059    4.204    0.000    0.248    0.252
##     SPJ        (b)    0.107    0.057    1.884    0.060    0.107    0.118
##     Male_splt         0.171    0.074    2.319    0.020    0.171    0.081
##     regn_splt         0.047    0.077    0.607    0.544    0.047    0.021
##     SDS               0.405    0.156    2.596    0.009    0.405    0.095
##   SPJ ~                                                                 
##     VC1       (a1)    1.197    0.070   17.026    0.000    1.197    0.481
##     VC2       (a2)    2.330    0.071   32.701    0.000    2.330    0.922
##     LC        (a3)   -0.088    0.044   -2.008    0.045   -0.088   -0.063
##     ExPJ      (a4)    0.115    0.030    3.795    0.000    0.115    0.102
##     GPJ       (a5)    0.137    0.037    3.749    0.000    0.137    0.126
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NL                0.666    0.042   15.827    0.000    0.666    0.592
##    .SPJ               0.416    0.026   15.827    0.000    0.416    0.299
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     indirct_VC1_NL    0.128    0.068    1.873    0.061    0.128    0.057
##     indirct_VC2_NL    0.248    0.132    1.881    0.060    0.248    0.109
##     indirect_LC_NL   -0.009    0.007   -1.374    0.169   -0.009   -0.008
##     indrct_ExPJ_NL    0.012    0.007    1.688    0.091    0.012    0.012
##     indirct_GPJ_NL    0.015    0.009    1.684    0.092    0.015    0.015
##                  npar                  fmin                 chisq 
##                18.000                 0.003                 3.119 
##                    df                pvalue        baseline.chisq 
##                 5.000                 0.682               871.105 
##           baseline.df       baseline.pvalue                   cfi 
##                21.000                 0.000                 1.000 
##                   tli                  nnfi                   rfi 
##                 1.009                 1.009                 0.985 
##                   nfi                  pnfi                   ifi 
##                 0.996                 0.237                 1.002 
##                   rni                  logl     unrestricted.logl 
##                 1.002             -1100.028             -1098.469 
##                   aic                   bic                ntotal 
##              2236.057              2311.956               501.000 
##                  bic2                 rmsea        rmsea.ci.lower 
##              2254.822                 0.000                 0.000 
##        rmsea.ci.upper        rmsea.ci.level          rmsea.pvalue 
##                 0.048                 0.900                 0.957 
##        rmsea.close.h0 rmsea.notclose.pvalue     rmsea.notclose.h0 
##                 0.050                 0.001                 0.080 
##                   rmr            rmr_nomean                  srmr 
##                 0.003                 0.003                 0.005 
##          srmr_bentler   srmr_bentler_nomean                  crmr 
##                 0.005                 0.005                 0.005 
##           crmr_nomean            srmr_mplus     srmr_mplus_nomean 
##                 0.005                 0.005                 0.005 
##                 cn_05                 cn_01                   gfi 
##              1779.330              2424.412                 0.998 
##                  agfi                  pgfi                   mfi 
##                 0.976                 0.064                 1.002 
##                  ecvi 
##                 0.078

AbbreviationFull_Name
VC1Neutral Video
VC2Positive Video
PEPolice Effectiveness
DJDistributive Justice
LCLegal Cynicism
GPJGlobal PJ
ExPJExpected PJ
SPJSpecific PJ
NLNormative Legitimacy
Male_splitMale
region_splitRegion
SDSSDS
Significant Pathways
FromToStd_EstimateP_Value
DJNL0.16703730.0061493
ExPJNL0.16919150.0000153
GPJNL0.25237580.0000262
Male_splitNL0.08068650.0203738
SDSNL0.09545000.0094184
VC1SPJ0.48056790.0000000
VC2SPJ0.92230200.0000000
LCSPJ-0.06346830.0446082
ExPJSPJ0.10227240.0001477
GPJSPJ0.12580410.0001775

6.2 Path Analysis 2 - Normative Legitimacy with GPJ Moderation and controls: Male, Region, SDS

## lavaan 0.6.17 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           501
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 4.024
##   Degrees of freedom                                 7
##   P-value (Chi-square)                           0.777
## 
## Model Test Baseline Model:
## 
##   Test statistic                               879.411
##   Degrees of freedom                                25
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.012
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -1096.328
##   Loglikelihood unrestricted model (H1)      -1094.316
##                                                       
##   Akaike (AIC)                                2232.655
##   Bayesian (BIC)                              2316.988
##   Sample-size adjusted Bayesian (SABIC)       2253.506
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.037
##   P-value H_0: RMSEA <= 0.050                    0.986
##   P-value H_0: RMSEA >= 0.080                    0.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.004
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   NL ~                                                                  
##     VC1       (c1)    0.064    0.113    0.568    0.570    0.064    0.029
##     VC2       (c2)   -0.071    0.160   -0.442    0.659   -0.071   -0.031
##     PE                0.068    0.054    1.250    0.211    0.068    0.072
##     DJ                0.137    0.050    2.740    0.006    0.137    0.167
##     LC               -0.048    0.059   -0.817    0.414   -0.048   -0.039
##     ExPJ              0.172    0.040    4.324    0.000    0.172    0.169
##     GPJ               0.248    0.059    4.202    0.000    0.248    0.252
##     SPJ        (b)    0.107    0.057    1.884    0.060    0.107    0.119
##     Male              0.171    0.074    2.319    0.020    0.171    0.081
##     Region            0.047    0.077    0.607    0.544    0.047    0.021
##     SDS               0.405    0.156    2.596    0.009    0.405    0.095
##   SPJ ~                                                                 
##     VC1       (a1)    0.951    0.151    6.310    0.000    0.951    0.382
##     VC2       (a2)    2.435    0.148   16.491    0.000    2.435    0.964
##     LC        (a3)   -0.079    0.044   -1.808    0.071   -0.079   -0.057
##     ExPJ      (a4)    0.118    0.030    3.887    0.000    0.118    0.104
##     GPJ       (a5)    0.118    0.052    2.295    0.022    0.118    0.108
##     VC1_GPJ   (i1)    0.119    0.065    1.827    0.068    0.119    0.118
##     VC2_GPJ   (i2)   -0.055    0.066   -0.836    0.403   -0.055   -0.051
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NL                0.666    0.042   15.827    0.000    0.666    0.592
##    .SPJ               0.410    0.026   15.827    0.000    0.410    0.294
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     indirct_VC1_NL    0.101    0.056    1.806    0.071    0.101    0.045
##     indirct_VC2_NL    0.259    0.139    1.872    0.061    0.259    0.114
##     indirect_LC_NL   -0.008    0.006   -1.304    0.192   -0.008   -0.007
##     indrct_ExPJ_NL    0.013    0.007    1.696    0.090    0.013    0.012
##     indirct_GPJ_NL    0.013    0.009    1.456    0.145    0.013    0.013
##     in_VC1_GPJ_SPJ    0.013    0.010    1.312    0.190    0.013    0.014
##     in_VC2_GPJ_SPJ   -0.006    0.008   -0.764    0.445   -0.006   -0.006
##                  npar                  fmin                 chisq 
##                20.000                 0.004                 4.024 
##                    df                pvalue        baseline.chisq 
##                 7.000                 0.777               879.411 
##           baseline.df       baseline.pvalue                   cfi 
##                25.000                 0.000                 1.000 
##                   tli                  nnfi                   rfi 
##                 1.012                 1.012                 0.984 
##                   nfi                  pnfi                   ifi 
##                 0.995                 0.279                 1.003 
##                   rni                  logl     unrestricted.logl 
##                 1.003             -1096.328             -1094.316 
##                   aic                   bic                ntotal 
##              2232.655              2316.988               501.000 
##                  bic2                 rmsea        rmsea.ci.lower 
##              2253.506                 0.000                 0.000 
##        rmsea.ci.upper        rmsea.ci.level          rmsea.pvalue 
##                 0.037                 0.900                 0.986 
##        rmsea.close.h0 rmsea.notclose.pvalue     rmsea.notclose.h0 
##                 0.050                 0.000                 0.080 
##                   rmr            rmr_nomean                  srmr 
##                 0.003                 0.003                 0.004 
##          srmr_bentler   srmr_bentler_nomean                  crmr 
##                 0.004                 0.004                 0.004 
##           crmr_nomean            srmr_mplus     srmr_mplus_nomean 
##                 0.004                 0.004                 0.004 
##                 cn_05                 cn_01                   gfi 
##              1752.517              2301.383                 0.998 
##                  agfi                  pgfi                   mfi 
##                 0.971                 0.067                 1.003 
##                  ecvi 
##                 0.088

AbbreviationFull_Name
VC1Neutral Video
VC2Positive Video
PEPolice Effectiveness
DJDistributive Justice
LCLegal Cynicism
GPJGlobal PJ
SPJSpecific PJ
NLNormative Legitimacy
ExPJExpected PJ
SDSSDS
MaleMale
RegionRegion
VC1_GPJNeut Vid * GPJ
VC2_GPJPos Vid * GPJ
Significant Pathways
ToFromStd_EstimateP_Value
NLDJ0.16704770.0061493
NLExPJ0.16920210.0000153
NLGPJ0.25239150.0000265
NLMale0.08069160.0203738
NLSDS0.09545600.0094184
SPJVC10.38168520.0000000
SPJVC20.96417860.0000000
SPJExPJ0.10446020.0001013
SPJGPJ0.10826980.0217309

6.3 Path Analysis 3 - Legitimacy with ExPJ Mod, & Controls: Male, Region, & SDS

## lavaan 0.6.17 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           501
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 3.405
##   Degrees of freedom                                 7
##   P-value (Chi-square)                           0.845
## 
## Model Test Baseline Model:
## 
##   Test statistic                               880.585
##   Degrees of freedom                                25
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.015
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -1095.432
##   Loglikelihood unrestricted model (H1)      -1093.729
##                                                       
##   Akaike (AIC)                                2230.863
##   Bayesian (BIC)                              2315.195
##   Sample-size adjusted Bayesian (SABIC)       2251.714
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.031
##   P-value H_0: RMSEA <= 0.050                    0.992
##   P-value H_0: RMSEA >= 0.080                    0.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.004
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   NL ~                                                                  
##     VC1       (c1)    0.064    0.113    0.568    0.570    0.064    0.029
##     VC2       (c2)   -0.071    0.160   -0.442    0.659   -0.071   -0.031
##     PE                0.068    0.054    1.251    0.211    0.068    0.072
##     DJ                0.137    0.050    2.740    0.006    0.137    0.167
##     LC               -0.048    0.059   -0.817    0.414   -0.048   -0.039
##     ExPJ              0.172    0.040    4.324    0.000    0.172    0.169
##     GPJ               0.248    0.059    4.203    0.000    0.248    0.252
##     SPJ        (b)    0.107    0.057    1.884    0.060    0.107    0.118
##     Male              0.171    0.074    2.319    0.020    0.171    0.081
##     Region            0.047    0.077    0.607    0.544    0.047    0.021
##     SDS               0.405    0.156    2.596    0.009    0.405    0.095
##   SPJ ~                                                                 
##     VC1       (a1)    0.560    0.222    2.523    0.012    0.560    0.225
##     VC2       (a2)    1.985    0.217    9.161    0.000    1.985    0.786
##     LC        (a3)   -0.076    0.044   -1.743    0.081   -0.076   -0.055
##     ExPJ      (a4)    0.016    0.047    0.336    0.737    0.016    0.014
##     GPJ       (a5)    0.148    0.036    4.048    0.000    0.148    0.135
##     VC1_ExPJ  (i1)    0.203    0.067    3.026    0.002    0.203    0.279
##     VC2_ExPJ  (i2)    0.112    0.066    1.697    0.090    0.112    0.150
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NL                0.666    0.042   15.827    0.000    0.666    0.592
##    .SPJ               0.408    0.026   15.827    0.000    0.408    0.293
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     indirct_VC1_NL    0.060    0.040    1.510    0.131    0.060    0.027
##     indirct_VC2_NL    0.211    0.115    1.846    0.065    0.211    0.093
##     indirect_LC_NL   -0.008    0.006   -1.279    0.201   -0.008   -0.006
##     indrct_ExPJ_NL    0.002    0.005    0.331    0.741    0.002    0.002
##     indirct_GPJ_NL    0.016    0.009    1.708    0.088    0.016    0.016
##     in_VC1_EPJ_SPJ    0.022    0.013    1.600    0.110    0.022    0.033
##     in_VC2_EPJ_SPJ    0.012    0.009    1.261    0.207    0.012    0.018
##                  npar                  fmin                 chisq 
##                20.000                 0.003                 3.405 
##                    df                pvalue        baseline.chisq 
##                 7.000                 0.845               880.585 
##           baseline.df       baseline.pvalue                   cfi 
##                25.000                 0.000                 1.000 
##                   tli                  nnfi                   rfi 
##                 1.015                 1.015                 0.986 
##                   nfi                  pnfi                   ifi 
##                 0.996                 0.279                 1.004 
##                   rni                  logl     unrestricted.logl 
##                 1.004             -1095.432             -1093.729 
##                   aic                   bic                ntotal 
##              2230.863              2315.195               501.000 
##                  bic2                 rmsea        rmsea.ci.lower 
##              2251.714                 0.000                 0.000 
##        rmsea.ci.upper        rmsea.ci.level          rmsea.pvalue 
##                 0.031                 0.900                 0.992 
##        rmsea.close.h0 rmsea.notclose.pvalue     rmsea.notclose.h0 
##                 0.050                 0.000                 0.080 
##                   rmr            rmr_nomean                  srmr 
##                 0.003                 0.003                 0.004 
##          srmr_bentler   srmr_bentler_nomean                  crmr 
##                 0.004                 0.004                 0.004 
##           crmr_nomean            srmr_mplus     srmr_mplus_nomean 
##                 0.004                 0.004                 0.004 
##                 cn_05                 cn_01                   gfi 
##              2070.541              2719.066                 0.998 
##                  agfi                  pgfi                   mfi 
##                 0.976                 0.067                 1.004 
##                  ecvi 
##                 0.087

AbbreviationFull_Name
VC1Neutral Video
VC2Positive Video
PEPolice Effectiveness
DJDistributive Justice
LCLegal Cynicism
GPJGlobal PJ
SPJSpecific PJ
NLNormative Legitimacy
ExPJExpected PJ
SDSSDS
MaleMale
RegionRegion
VC1_ExPJNeut Vid * ExPJ
VC2_ExPJPos Vid * ExPJ
Significant Pathways
ToFromStd_EstimateP_Value
NLDJ0.16704240.0061493
NLExPJ0.16919670.0000153
NLGPJ0.25238350.0000263
NLMale0.08068900.0203740
NLSDS0.09545290.0094184
SPJVC10.22487640.0116257
SPJVC20.78590740.0000000
SPJGPJ0.13521250.0000516
SPJVC1_ExPJ0.27925190.0024805

6.4 Path Analysis 4 - Legitimacy with ExPJ Mod, & Controls: Male & SDS

## lavaan 0.6.17 ended normally after 2 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        19
## 
##   Number of observations                           501
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 2.279
##   Degrees of freedom                                 6
##   P-value (Chi-square)                           0.892
## 
## Model Test Baseline Model:
## 
##   Test statistic                               879.091
##   Degrees of freedom                                23
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.017
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -1095.615
##   Loglikelihood unrestricted model (H1)      -1094.476
##                                                       
##   Akaike (AIC)                                2229.231
##   Bayesian (BIC)                              2309.346
##   Sample-size adjusted Bayesian (SABIC)       2249.039
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.026
##   P-value H_0: RMSEA <= 0.050                    0.994
##   P-value H_0: RMSEA >= 0.080                    0.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.003
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   NL ~                                                                  
##     VC1       (c1)    0.062    0.113    0.546    0.585    0.062    0.028
##     VC2       (c2)   -0.073    0.160   -0.458    0.647   -0.073   -0.032
##     PE                0.066    0.054    1.225    0.221    0.066    0.070
##     DJ                0.138    0.050    2.767    0.006    0.138    0.169
##     LC               -0.050    0.059   -0.840    0.401   -0.050   -0.040
##     ExPJ              0.171    0.040    4.318    0.000    0.171    0.169
##     GPJ               0.247    0.059    4.189    0.000    0.247    0.252
##     SPJ        (b)    0.105    0.057    1.855    0.064    0.105    0.117
##     Male_splt         0.173    0.074    2.351    0.019    0.173    0.082
##     SDS               0.402    0.156    2.578    0.010    0.402    0.095
##   SPJ ~                                                                 
##     VC1       (a1)    0.560    0.222    2.523    0.012    0.560    0.225
##     VC2       (a2)    1.985    0.217    9.161    0.000    1.985    0.786
##     LC        (a3)   -0.076    0.044   -1.743    0.081   -0.076   -0.055
##     ExPJ      (a4)    0.016    0.047    0.336    0.737    0.016    0.014
##     GPJ       (a5)    0.148    0.036    4.048    0.000    0.148    0.135
##     VC1_ExPJ  (i1)    0.203    0.067    3.026    0.002    0.203    0.279
##     VC2_ExPJ  (i2)    0.112    0.066    1.697    0.090    0.112    0.150
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NL                0.666    0.042   15.827    0.000    0.666    0.593
##    .SPJ               0.408    0.026   15.827    0.000    0.408    0.293
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     indirct_VC1_NL    0.059    0.039    1.495    0.135    0.059    0.026
##     indirct_VC2_NL    0.208    0.115    1.818    0.069    0.208    0.092
##     indirect_LC_NL   -0.008    0.006   -1.270    0.204   -0.008   -0.006
##     indrct_ExPJ_NL    0.002    0.005    0.331    0.741    0.002    0.002
##     indirct_GPJ_NL    0.015    0.009    1.686    0.092    0.015    0.016
##     in_VC1_EPJ_SPJ    0.021    0.013    1.581    0.114    0.021    0.033
##     in_VC2_EPJ_SPJ    0.012    0.009    1.252    0.211    0.012    0.017
##                  npar                  fmin                 chisq 
##                19.000                 0.002                 2.279 
##                    df                pvalue        baseline.chisq 
##                 6.000                 0.892               879.091 
##           baseline.df       baseline.pvalue                   cfi 
##                23.000                 0.000                 1.000 
##                   tli                  nnfi                   rfi 
##                 1.017                 1.017                 0.990 
##                   nfi                  pnfi                   ifi 
##                 0.997                 0.260                 1.004 
##                   rni                  logl     unrestricted.logl 
##                 1.004             -1095.615             -1094.476 
##                   aic                   bic                ntotal 
##              2229.231              2309.346               501.000 
##                  bic2                 rmsea        rmsea.ci.lower 
##              2249.039                 0.000                 0.000 
##        rmsea.ci.upper        rmsea.ci.level          rmsea.pvalue 
##                 0.026                 0.900                 0.994 
##        rmsea.close.h0 rmsea.notclose.pvalue     rmsea.notclose.h0 
##                 0.050                 0.000                 0.080 
##                   rmr            rmr_nomean                  srmr 
##                 0.003                 0.003                 0.003 
##          srmr_bentler   srmr_bentler_nomean                  crmr 
##                 0.003                 0.003                 0.004 
##           crmr_nomean            srmr_mplus     srmr_mplus_nomean 
##                 0.004                 0.003                 0.003 
##                 cn_05                 cn_01                   gfi 
##              2769.543              3697.472                 0.999 
##                  agfi                  pgfi                   mfi 
##                 0.983                 0.066                 1.004 
##                  ecvi 
##                 0.080

AbbreviationFull_Name
VC1Neutral Video
VC2Positive Video
PEPolice Effectiveness
DJDistributive Justice
LCLegal Cynicism
GPJGlobal PJ
SPJSpecific PJ
NLNormative Legitimacy
ExPJExpected PJ
SDSSDS
Male_splitMale Split
VC1_ExPJNeut Vid * ExPJ
VC2_ExPJPos Vid * ExPJ
Significant Pathways
ToFromStd_EstimateP_Value
NLDJ0.16862030.0056580
NLExPJ0.16903190.0000157
NLGPJ0.25158300.0000280
NLMale_split0.08173490.0187210
NLSDS0.09478010.0099297
SPJVC10.22487640.0116257
SPJVC20.78590740.0000000
SPJGPJ0.13521250.0000516
SPJVC1_ExPJ0.27925190.0024805

6.4.1 Path Analysis 4.5 - Legitimacy with ExPJ Mod, & Controls: Male & SDS

## lavaan 0.6.17 ended normally after 3 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        16
## 
##   Number of observations                           501
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 1.947
##   Degrees of freedom                                 5
##   P-value (Chi-square)                           0.856
## 
## Model Test Baseline Model:
## 
##   Test statistic                               873.512
##   Degrees of freedom                                19
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.014
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -1098.239
##   Loglikelihood unrestricted model (H1)      -1097.265
##                                                       
##   Akaike (AIC)                                2228.478
##   Bayesian (BIC)                              2295.943
##   Sample-size adjusted Bayesian (SABIC)       2245.158
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.034
##   P-value H_0: RMSEA <= 0.050                    0.987
##   P-value H_0: RMSEA >= 0.080                    0.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.004
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   NL ~                                                                  
##     VC1       (c1)    0.069    0.113    0.613    0.540    0.069    0.031
##     VC2       (c2)   -0.086    0.160   -0.537    0.591   -0.086   -0.038
##     DJ                0.176    0.043    4.086    0.000    0.176    0.215
##     ExPJ              0.170    0.040    4.285    0.000    0.170    0.168
##     GPJ               0.286    0.053    5.413    0.000    0.286    0.291
##     SPJ        (b)    0.111    0.056    1.973    0.049    0.111    0.124
##     Male_splt         0.174    0.074    2.349    0.019    0.174    0.082
##     SDS               0.431    0.153    2.810    0.005    0.431    0.102
##   SPJ ~                                                                 
##     VC1       (a1)    0.538    0.222    2.420    0.015    0.538    0.216
##     VC2       (a2)    1.978    0.217    9.104    0.000    1.978    0.783
##     ExPJ      (a4)    0.010    0.047    0.219    0.827    0.010    0.009
##     GPJ       (a5)    0.186    0.029    6.338    0.000    0.186    0.170
##     VC1_ExPJ  (i1)    0.213    0.067    3.186    0.001    0.213    0.294
##     VC2_ExPJ  (i2)    0.118    0.066    1.779    0.075    0.118    0.157
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NL                0.669    0.042   15.827    0.000    0.669    0.596
##    .SPJ               0.411    0.026   15.827    0.000    0.411    0.295
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     indirct_VC1_NL    0.060    0.039    1.529    0.126    0.060    0.027
##     indirct_VC2_NL    0.220    0.114    1.928    0.054    0.220    0.097
##     indrct_ExPJ_NL    0.001    0.005    0.217    0.828    0.001    0.001
##     indirct_GPJ_NL    0.021    0.011    1.884    0.060    0.021    0.021
##     in_VC1_EPJ_SPJ    0.024    0.014    1.677    0.093    0.024    0.036
##     in_VC2_EPJ_SPJ    0.013    0.010    1.321    0.186    0.013    0.019
##                  npar                  fmin                 chisq 
##                16.000                 0.002                 1.947 
##                    df                pvalue        baseline.chisq 
##                 5.000                 0.856               873.512 
##           baseline.df       baseline.pvalue                   cfi 
##                19.000                 0.000                 1.000 
##                   tli                  nnfi                   rfi 
##                 1.014                 1.014                 0.992 
##                   nfi                  pnfi                   ifi 
##                 0.998                 0.263                 1.004 
##                   rni                  logl     unrestricted.logl 
##                 1.004             -1098.239             -1097.265 
##                   aic                   bic                ntotal 
##              2228.478              2295.943               501.000 
##                  bic2                 rmsea        rmsea.ci.lower 
##              2245.158                 0.000                 0.000 
##        rmsea.ci.upper        rmsea.ci.level          rmsea.pvalue 
##                 0.034                 0.900                 0.987 
##        rmsea.close.h0 rmsea.notclose.pvalue     rmsea.notclose.h0 
##                 0.050                 0.000                 0.080 
##                   rmr            rmr_nomean                  srmr 
##                 0.003                 0.003                 0.004 
##          srmr_bentler   srmr_bentler_nomean                  crmr 
##                 0.004                 0.004                 0.004 
##           crmr_nomean            srmr_mplus     srmr_mplus_nomean 
##                 0.004                 0.004                 0.004 
##                 cn_05                 cn_01                   gfi 
##              2849.336              3882.557                 0.999 
##                  agfi                  pgfi                   mfi 
##                 0.988                 0.076                 1.003 
##                  ecvi 
##                 0.068

AbbreviationFull_Name
VC1Neutral Video
VC2Positive Video
DJDistributive Justice
GPJGlobal PJ
SPJSpecific PJ
NLNormative Legitimacy
ExPJExpected PJ
SDSSDS
Male_splitMale Split
VC1_ExPJNeut Vid * ExPJ
VC2_ExPJPos Vid * ExPJ
Significant Pathways
ToFromStd_EstimateP_Value
NLDJ0.21450940.0000439
NLExPJ0.16764990.0000183
NLGPJ0.29136450.0000001
NLSPJ0.12391860.0485139
NLMale_split0.08186780.0188158
NLSDS0.10163950.0049486
SPJVC10.21601380.0154994
SPJVC20.78319610.0000000
SPJGPJ0.17005610.0000000
SPJVC1_ExPJ0.29375620.0014413

6.4.2 Path Analysis 4.5 - Cross-validation

## lavaan 0.6.17 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        16
## 
##   Number of observations                           251
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 1.531
##   Degrees of freedom                                 5
##   P-value (Chi-square)                           0.909
## 
## Model Test Baseline Model:
## 
##   Test statistic                               437.658
##   Degrees of freedom                                19
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.031
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)               -543.882
##   Loglikelihood unrestricted model (H1)       -543.117
##                                                       
##   Akaike (AIC)                                1119.765
##   Bayesian (BIC)                              1176.172
##   Sample-size adjusted Bayesian (SABIC)       1125.450
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.034
##   P-value H_0: RMSEA <= 0.050                    0.974
##   P-value H_0: RMSEA >= 0.080                    0.004
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.004
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   NL ~                                                                  
##     VC1       (c1)    0.152    0.157    0.969    0.333    0.152    0.071
##     VC2       (c2)   -0.065    0.225   -0.290    0.772   -0.065   -0.030
##     DJ                0.214    0.060    3.553    0.000    0.214    0.265
##     ExPJ              0.175    0.056    3.148    0.002    0.175    0.178
##     GPJ               0.215    0.075    2.880    0.004    0.215    0.226
##     SPJ        (b)    0.114    0.077    1.476    0.140    0.114    0.130
##     Male_splt         0.110    0.102    1.074    0.283    0.110    0.053
##     SDS               0.434    0.206    2.108    0.035    0.434    0.106
##   SPJ ~                                                                 
##     VC1       (a1)    0.469    0.316    1.485    0.138    0.469    0.192
##     VC2       (a2)    2.018    0.301    6.703    0.000    2.018    0.806
##     ExPJ      (a4)    0.009    0.072    0.121    0.904    0.009    0.008
##     GPJ       (a5)    0.193    0.042    4.567    0.000    0.193    0.178
##     VC1_ExPJ  (i1)    0.243    0.097    2.496    0.013    0.243    0.339
##     VC2_ExPJ  (i2)    0.128    0.094    1.358    0.174    0.128    0.169
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NL                0.634    0.057   11.203    0.000    0.634    0.592
##    .SPJ               0.412    0.037   11.203    0.000    0.412    0.297
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     indirct_VC1_NL    0.054    0.051    1.047    0.295    0.054    0.025
##     indirct_VC2_NL    0.230    0.160    1.442    0.149    0.230    0.105
##     indrct_ExPJ_NL    0.001    0.008    0.120    0.904    0.001    0.001
##     indirct_GPJ_NL    0.022    0.016    1.405    0.160    0.022    0.023
##     in_VC1_EPJ_SPJ    0.028    0.022    1.271    0.204    0.028    0.044
##     in_VC2_EPJ_SPJ    0.015    0.015    1.000    0.318    0.015    0.022
##      [,1]

6.4.3 Path Analysis 4.5 - Bootstrap Validation

## lavaan 0.6.17 ended normally after 3 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        16
## 
##   Number of observations                           501
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 1.947
##   Degrees of freedom                                 5
##   P-value (Chi-square)                           0.856
## 
## Model Test Baseline Model:
## 
##   Test statistic                               873.512
##   Degrees of freedom                                19
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.014
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -1098.239
##   Loglikelihood unrestricted model (H1)      -1097.265
##                                                       
##   Akaike (AIC)                                2228.478
##   Bayesian (BIC)                              2295.943
##   Sample-size adjusted Bayesian (SABIC)       2245.158
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.034
##   P-value H_0: RMSEA <= 0.050                    0.987
##   P-value H_0: RMSEA >= 0.080                    0.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.004
## 
## Parameter Estimates:
## 
##   Standard errors                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws            1000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   NL ~                                                                  
##     VC1       (c1)    0.069    0.111    0.622    0.534    0.069    0.031
##     VC2       (c2)   -0.086    0.161   -0.535    0.593   -0.086   -0.038
##     DJ                0.176    0.047    3.747    0.000    0.176    0.215
##     ExPJ              0.170    0.048    3.548    0.000    0.170    0.168
##     GPJ               0.286    0.061    4.664    0.000    0.286    0.291
##     SPJ        (b)    0.111    0.059    1.872    0.061    0.111    0.124
##     Male_splt         0.174    0.074    2.346    0.019    0.174    0.082
##     SDS               0.431    0.168    2.563    0.010    0.431    0.102
##   SPJ ~                                                                 
##     VC1       (a1)    0.538    0.212    2.541    0.011    0.538    0.216
##     VC2       (a2)    1.978    0.165   11.974    0.000    1.978    0.783
##     ExPJ      (a4)    0.010    0.039    0.262    0.794    0.010    0.009
##     GPJ       (a5)    0.186    0.032    5.744    0.000    0.186    0.170
##     VC1_ExPJ  (i1)    0.213    0.066    3.227    0.001    0.213    0.294
##     VC2_ExPJ  (i2)    0.118    0.052    2.256    0.024    0.118    0.157
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NL                0.669    0.048   13.863    0.000    0.669    0.596
##    .SPJ               0.411    0.034   12.078    0.000    0.411    0.295
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     indirct_VC1_NL    0.060    0.041    1.464    0.143    0.060    0.027
##     indirct_VC2_NL    0.220    0.118    1.860    0.063    0.220    0.097
##     indrct_ExPJ_NL    0.001    0.005    0.226    0.821    0.001    0.001
##     indirct_GPJ_NL    0.021    0.012    1.769    0.077    0.021    0.021
##     in_VC1_EPJ_SPJ    0.024    0.016    1.522    0.128    0.024    0.036
##     in_VC2_EPJ_SPJ    0.013    0.010    1.359    0.174    0.013    0.019

6.5 Path Analysis 5 - Legitimacy with ExPJ & GPJ Mod, & Controls: Male & SDS

## lavaan 0.6.17 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        21
## 
##   Number of observations                           501
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 3.554
##   Degrees of freedom                                 8
##   P-value (Chi-square)                           0.895
## 
## Model Test Baseline Model:
## 
##   Test statistic                               886.685
##   Degrees of freedom                                27
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.017
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -1092.456
##   Loglikelihood unrestricted model (H1)      -1090.679
##                                                       
##   Akaike (AIC)                                2226.912
##   Bayesian (BIC)                              2315.461
##   Sample-size adjusted Bayesian (SABIC)       2248.805
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.023
##   P-value H_0: RMSEA <= 0.050                    0.997
##   P-value H_0: RMSEA >= 0.080                    0.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.003
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   NL ~                                                                  
##     VC1       (c1)    0.062    0.113    0.546    0.585    0.062    0.028
##     VC2       (c2)   -0.073    0.160   -0.458    0.647   -0.073   -0.032
##     PE                0.066    0.054    1.225    0.221    0.066    0.070
##     DJ                0.138    0.050    2.767    0.006    0.138    0.169
##     LC               -0.050    0.059   -0.840    0.401   -0.050   -0.040
##     ExPJ              0.171    0.040    4.318    0.000    0.171    0.169
##     GPJ               0.247    0.059    4.188    0.000    0.247    0.252
##     SPJ        (b)    0.105    0.057    1.855    0.064    0.105    0.117
##     Male_splt         0.173    0.074    2.351    0.019    0.173    0.082
##     SDS               0.402    0.156    2.578    0.010    0.402    0.095
##   SPJ ~                                                                 
##     VC1       (a1)    0.522    0.226    2.314    0.021    0.522    0.210
##     VC2       (a2)    2.074    0.220    9.440    0.000    2.074    0.821
##     LC        (a3)   -0.075    0.044   -1.708    0.088   -0.075   -0.054
##     ExPJ      (a4)   -0.001    0.052   -0.026    0.979   -0.001   -0.001
##     GPJ       (a5)    0.183    0.056    3.267    0.001    0.183    0.167
##     VC1_ExPJ  (i1)    0.193    0.074    2.614    0.009    0.193    0.266
##     VC2_ExPJ  (i2)    0.168    0.073    2.299    0.022    0.168    0.225
##     VC1_GPJ   (i3)    0.033    0.072    0.455    0.649    0.033    0.032
##     VC2_GPJ   (i4)   -0.134    0.073   -1.849    0.064   -0.134   -0.125
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NL                0.666    0.042   15.827    0.000    0.666    0.593
##    .SPJ               0.403    0.025   15.827    0.000    0.403    0.290
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     indirct_VC1_NL    0.055    0.038    1.447    0.148    0.055    0.024
##     indirct_VC2_NL    0.217    0.120    1.820    0.069    0.217    0.096
##     indirect_LC_NL   -0.008    0.006   -1.256    0.209   -0.008   -0.006
##     indrct_ExPJ_NL   -0.000    0.005   -0.026    0.979   -0.000   -0.000
##     indirct_GPJ_NL    0.019    0.012    1.613    0.107    0.019    0.020
##     in_VC1_EPJ_SPJ    0.020    0.013    1.513    0.130    0.020    0.031
##     in_VC2_EPJ_SPJ    0.018    0.012    1.443    0.149    0.018    0.026
##     in_VC1_GPJ_SPJ    0.003    0.008    0.442    0.659    0.003    0.004
##     in_VC2_GPJ_SPJ   -0.014    0.011   -1.310    0.190   -0.014   -0.015
##                  npar                  fmin                 chisq 
##                21.000                 0.004                 3.554 
##                    df                pvalue        baseline.chisq 
##                 8.000                 0.895               886.685 
##           baseline.df       baseline.pvalue                   cfi 
##                27.000                 0.000                 1.000 
##                   tli                  nnfi                   rfi 
##                 1.017                 1.017                 0.986 
##                   nfi                  pnfi                   ifi 
##                 0.996                 0.295                 1.005 
##                   rni                  logl     unrestricted.logl 
##                 1.005             -1092.456             -1090.679 
##                   aic                   bic                ntotal 
##              2226.912              2315.461               501.000 
##                  bic2                 rmsea        rmsea.ci.lower 
##              2248.805                 0.000                 0.000 
##        rmsea.ci.upper        rmsea.ci.level          rmsea.pvalue 
##                 0.023                 0.900                 0.997 
##        rmsea.close.h0 rmsea.notclose.pvalue     rmsea.notclose.h0 
##                 0.050                 0.000                 0.080 
##                   rmr            rmr_nomean                  srmr 
##                 0.003                 0.003                 0.003 
##          srmr_bentler   srmr_bentler_nomean                  crmr 
##                 0.003                 0.003                 0.004 
##           crmr_nomean            srmr_mplus     srmr_mplus_nomean 
##                 0.004                 0.003                 0.003 
##                 cn_05                 cn_01                   gfi 
##              2186.864              2832.859                 0.998 
##                  agfi                  pgfi                   mfi 
##                 0.975                 0.067                 1.004 
##                  ecvi 
##                 0.091

AbbreviationFull_Name
VC1Neutral Video
VC2Positive Video
PEPolice Effectiveness
DJDistributive Justice
LCLegal Cynicism
GPJGlobal PJ
SPJSpecific PJ
NLNormative Legitimacy
ExPJExpected PJ
SDSSDS
Male_splitMale Split
VC1_ExPJNeut Vid * ExPJ
VC2_ExPJPos Vid * ExPJ
VC1_GPJNeut Vid * GPJ
VC2_GPJPos Vid * GPJ
Significant Pathways
ToFromStd_EstimateP_Value
NLDJ0.16862720.0056580
NLExPJ0.16903880.0000157
NLGPJ0.25159330.0000282
NLMale_split0.08173820.0187215
NLSDS0.09478400.0099300
SPJVC10.20951910.0206482
SPJVC20.82093260.0000000
SPJGPJ0.16715790.0010875
SPJVC1_ExPJ0.26634420.0089575
SPJVC2_ExPJ0.22468570.0215273

6.6 Path Analysis 6 - Legitimacy & Non-normative Legitimacy with ExPJ Mod, & Controls: Male & SDS

## lavaan 0.6.17 ended normally after 5 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        33
## 
##   Number of observations                           501
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 4.905
##   Degrees of freedom                                 9
##   P-value (Chi-square)                           0.843
## 
## Model Test Baseline Model:
## 
##   Test statistic                              1031.291
##   Degrees of freedom                                39
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.018
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -1714.226
##   Loglikelihood unrestricted model (H1)      -1711.773
##                                                       
##   Akaike (AIC)                                3494.452
##   Bayesian (BIC)                              3633.600
##   Sample-size adjusted Bayesian (SABIC)       3528.855
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.029
##   P-value H_0: RMSEA <= 0.050                    0.996
##   P-value H_0: RMSEA >= 0.080                    0.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.004
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   NL ~                                                                  
##     VC1       (c1)    0.064    0.113    0.568    0.570    0.064    0.029
##     VC2       (c2)   -0.071    0.160   -0.442    0.659   -0.071   -0.031
##     PE                0.068    0.054    1.251    0.211    0.068    0.072
##     DJ                0.137    0.050    2.740    0.006    0.137    0.167
##     LC               -0.048    0.059   -0.817    0.414   -0.048   -0.039
##     ExPJ              0.172    0.040    4.324    0.000    0.172    0.169
##     GPJ               0.248    0.059    4.203    0.000    0.248    0.252
##     SPJ        (b)    0.107    0.057    1.884    0.060    0.107    0.118
##     Male              0.171    0.074    2.319    0.020    0.171    0.081
##     Reg               0.047    0.077    0.607    0.544    0.047    0.021
##     SDS               0.405    0.156    2.596    0.009    0.405    0.095
##   NNL ~                                                                 
##     VC1       (d1)   -0.014    0.116   -0.124    0.902   -0.014   -0.007
##     VC2       (d2)   -0.296    0.164   -1.805    0.071   -0.296   -0.143
##     PE               -0.102    0.055   -1.838    0.066   -0.102   -0.119
##     DJ                0.020    0.051    0.388    0.698    0.020    0.027
##     LC                0.176    0.061    2.897    0.004    0.176    0.154
##     ExPJ             -0.051    0.041   -1.263    0.206   -0.051   -0.056
##     GPJ              -0.164    0.060   -2.717    0.007   -0.164   -0.183
##     SPJ        (e)   -0.086    0.058   -1.496    0.135   -0.086   -0.106
##     Male              0.083    0.076    1.094    0.274    0.083    0.043
##     Reg              -0.041    0.079   -0.518    0.604   -0.041   -0.020
##     SDS              -0.260    0.159   -1.629    0.103   -0.260   -0.067
##   SPJ ~                                                                 
##     VC1       (a1)    0.560    0.222    2.523    0.012    0.560    0.225
##     VC2       (a2)    1.985    0.217    9.161    0.000    1.985    0.786
##     LC        (a3)   -0.076    0.044   -1.743    0.081   -0.076   -0.055
##     ExPJ      (a4)    0.016    0.047    0.336    0.737    0.016    0.014
##     GPJ       (a5)    0.148    0.036    4.048    0.000    0.148    0.135
##     VC1_ExPJ  (i1)    0.203    0.067    3.026    0.002    0.203    0.279
##     VC2_ExPJ  (i2)    0.112    0.066    1.697    0.090    0.112    0.150
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .NL ~~                                                                 
##    .NNL               0.053    0.031    1.730    0.084    0.053    0.078
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NL                0.666    0.042   15.827    0.000    0.666    0.592
##    .NNL               0.697    0.044   15.827    0.000    0.697    0.747
##    .SPJ               0.408    0.026   15.827    0.000    0.408    0.293
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     indirct_VC1_NL    0.060    0.040    1.510    0.131    0.060    0.027
##     indirct_VC2_NL    0.211    0.115    1.846    0.065    0.211    0.093
##     indirect_LC_NL   -0.008    0.006   -1.279    0.201   -0.008   -0.006
##     indrct_ExPJ_NL    0.002    0.005    0.331    0.741    0.002    0.002
##     indirct_GPJ_NL    0.016    0.009    1.708    0.088    0.016    0.016
##     indrct_VC1_NNL   -0.048    0.038   -1.287    0.198   -0.048   -0.024
##     indrct_VC2_NNL   -0.172    0.116   -1.476    0.140   -0.172   -0.083
##     indirct_LC_NNL    0.007    0.006    1.135    0.256    0.007    0.006
##     indrct_EPJ_NNL   -0.001    0.004   -0.328    0.743   -0.001   -0.001
##     indrct_GPJ_NNL   -0.013    0.009   -1.403    0.161   -0.013   -0.014
##     in_VC1_EPJ_SPJ    0.022    0.013    1.600    0.110    0.022    0.033
##     in_VC2_EPJ_SPJ    0.012    0.009    1.261    0.207    0.012    0.018
##                  npar                  fmin                 chisq 
##                33.000                 0.005                 4.905 
##                    df                pvalue        baseline.chisq 
##                 9.000                 0.843              1031.291 
##           baseline.df       baseline.pvalue                   cfi 
##                39.000                 0.000                 1.000 
##                   tli                  nnfi                   rfi 
##                 1.018                 1.018                 0.979 
##                   nfi                  pnfi                   ifi 
##                 0.995                 0.230                 1.004 
##                   rni                  logl     unrestricted.logl 
##                 1.004             -1714.226             -1711.773 
##                   aic                   bic                ntotal 
##              3494.452              3633.600               501.000 
##                  bic2                 rmsea        rmsea.ci.lower 
##              3528.855                 0.000                 0.000 
##        rmsea.ci.upper        rmsea.ci.level          rmsea.pvalue 
##                 0.029                 0.900                 0.996 
##        rmsea.close.h0 rmsea.notclose.pvalue     rmsea.notclose.h0 
##                 0.050                 0.000                 0.080 
##                   rmr            rmr_nomean                  srmr 
##                 0.003                 0.003                 0.004 
##          srmr_bentler   srmr_bentler_nomean                  crmr 
##                 0.004                 0.004                 0.004 
##           crmr_nomean            srmr_mplus     srmr_mplus_nomean 
##                 0.004                 0.004                 0.004 
##                 cn_05                 cn_01                   gfi 
##              1729.187              2214.071                 0.998 
##                  agfi                  pgfi                   mfi 
##                 0.974                 0.075                 1.004 
##                  ecvi 
##                 0.142
## Some edges involve nodes not in layout. These were dropped.

AbbreviationFull_Name
VC1Neutral Video
VC2Positive Video
PEPolice Effectiveness
DJDistributive Justice
LCLegal Cynicism
GPJGlobal PJ
SPJSpecific PJ
NLNormative Legitimacy
NNLNon-normative Legitimacy
ExPJExpected PJ
SDSSDS
MaleMale
VC1_ExPJNeut Vid * ExPJ
VC2_ExPJPos Vid * ExPJ
Significant Pathways
ToFromStd_EstimateP_Value
NLDJ0.16704240.0061493
NLExPJ0.16919670.0000153
NLGPJ0.25238350.0000263
NLMale0.08068900.0203740
NLSDS0.09545290.0094184
NNLLC0.15399050.0037635
NNLGPJ-0.18321110.0065970
SPJVC10.22487640.0116257
SPJVC20.78590740.0000000
SPJGPJ0.13521250.0000516
SPJVC1_ExPJ0.27925190.0024805

7 Ranked PJ

## `summarise()` has grouped output by 'rank_position'. You can override using the
## `.groups` argument.

8 Memory

Video condition 0 = Negative video

Video condition 1 = Neutral video

Video condition 2 = Positive video

## `summarise()` has grouped output by 'video_condition'. You can override using
## the `.groups` argument.

9 Text Analysis

9.1 Analysis for Condition 0

##   document negative positive sentiment
## 1        1        1        0        -1
## 2        2        1        0        -1
## 3        3        0        1         1
## 4        4        1        0        -1
## 5        5        1        0        -1
## 6        6        1        0        -1
##   document negative positive sentiment
## 1        1        1        0        -1
## 2        2        1        0        -1
## 3        3        0        1         1
## 4        4        0        1         1
## 5        5        0        1         1
## 6        6        0        1         1
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -1.0000 -1.0000 -1.0000 -0.4477  1.0000  1.0000
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -1.0000 -1.0000 -1.0000 -0.3911  1.0000  1.0000

## Joining with `by = join_by(word)`
## Joining with `by = join_by(word)`

9.2 Analysis for Condition 1

## Joining with `by = join_by(word)`

## Joining with `by = join_by(word)`

9.3 Analysis for Condition 2

## Joining with `by = join_by(word)`
## Joining with `by = join_by(word)`

10 Variable List

Variable Names and Descriptions
VariableDescription
  1. Duration
Duration (in seconds))
  1. dist_just1
The police provide the same level of security to all community members. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. dist_just2
The police provide the same quality of service to all community members. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. dist_just3
The police enforce the law consistently when dealing with all community members. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. dist_just4
The police deploy their resources in this city in an equitable manner. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. dist_just5
The police ensure that everyone has equal access to the services they provide. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. pol_effect1
The police do a good job working together with neighborhood residents to reduce crime (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. pol_effect2
The police do a good job dealing with neighborhood problems (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. pol_effect3
The police do a good job ExPJventing crime (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. leg_cyn1R
Laws protect everyone equally (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. leg_cyn2
People with money and power can get away with anything (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. leg_cyn3
Politicians only care about getting re-elected (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. leg_cyn4R
Anyone can get ahead if they try hard enough (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. leg_cyn5
Powerful people use laws to disadvantage individuals who do not have any power (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. expected_pj1
I expect the police to treat drivers with dignity and respect (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. expected_pj2
I expect the police to be polite when dealing with drivers (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. expected_pj3
I expect the police to be fair when making decisions with drivers (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. expected_pj4
I expect the police to give drivers the opportunity to exExPJss their views (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. expected_pj5
I expect the police to listen to drivers during stops (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. expected_pj6
I expect the police to make decisions based upon facts (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. global_pj1
The police treat people with dignity and respect (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. global_pj2
The police treat people with politeness (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. global_pj3
The police are fair when making decisions (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. global_pj4
The police give people the opportunity to exExPJss their views (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. global_pj5
The police take time to listen to people when they stop them (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. global_pj6
The police make decisions based upon facts (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. specific_pj1
The police officer treated the driver with dignity and respect (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. specific_pj2
The police officer was polite when dealing with the driver (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. specific_pj3
The police officer was fair when making the decision to the driver (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. specific_pj4
The police officer gave the driver the opportunity to exExPJss their views (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. specific_pj5
The police officer listened to the driver during the stop (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. specific_pj6
The police officer made decisions based upon facts (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. open_quest1_4
Please identify and describe up to five specific behaviors demonstrated by the officer in the video. - 1st response
  1. open_quest1_5
Please identify and describe up to five specific behaviors demonstrated by the officer in the video. - 2nd response
  1. open_quest1_6
Please identify and describe up to five specific behaviors demonstrated by the officer in the video. - 3rd response
  1. open_quest1_7
Please identify and describe up to five specific behaviors demonstrated by the officer in the video. - 4th response
  1. open_quest1_8
Please identify and describe up to five specific behaviors demonstrated by the officer in the video. - 5th response
  1. open_quest2
Overall, do you consider the outcome in the video to be just or unjust? Please explain why.
  1. SDS1R
I sometimes litter. (False = 0, True = 1)
  1. SDS2
I always admit my mistakes openly and face the potential negative consequences. (False = 0, True = 1)
  1. SDS3
In traffic I am always polite and considerate of others. (False = 0, True = 1)
  1. SDS4R
I have tried illegal drugs (for example, marijuana, cocaine, etc.). (False = 0, True = 1)
  1. SDS5
I always accept others’ opinions, even when they don’t agree with my own. (False = 0, True = 1)
  1. SDS6R
I take out my bad moods on others now and then. (False = 0, True = 1)
  1. SDS7R
There has been an occasion when I took advantage of someone else. (False = 0, True = 1)
  1. SDS8
In conversations I always listen attentively and let others finish their sentences. (False = 0, True = 1)
  1. SDS9
I never hesitate to help someone in case of emergency. (False = 0, True = 1)
  1. SDS10
When I have made a promise, I keep it–no ifs, ands or buts. (False = 0, True = 1)
  1. SDS11R
I occasionally speak badly of others behind their back. (False = 0, True = 1)
  1. SDS12
I would never live off other people. (False = 0, True = 1)
  1. SDS13
I always stay friendly and courteous with other people, even when I am stressed out. (False = 0, True = 1)
  1. SDS14
During arguments I always stay objective and matter-of-fact. (False = 0, True = 1)
  1. SDS15R
There has been at least one occasion when I failed to return an item that I borrowed. (False = 0, True = 1)
  1. SDS16
I always eat a healthy diet. (False = 0, True = 1)
  1. SDS17R
Sometimes I only help because I expect something in return. (False = 0, True = 1)
  1. norm_leg1
I would feel a moral obligation to obey the police. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. norm_leg2
I would feel a moral duty to obey the instructions of the police officer even if I don’t understand the reasons behind them. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. norm_leg3
I would feel a moral duty to support the decisions of the police officer, even if I disagree with them. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. norm_leg4
I would do what the police officer told me to do because I believe it is the right thing to do. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. norm_leg5
I believe that the proper thing to do is to accept the decisions that the police officer makes. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. nonnorm_leg1
People like me have no choice but to obey the police officer. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. nonnorm_leg2
If I didn’t do what the police officer told me, he would treat me badly. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. nonnorm_leg3
I would only obey the police officer because I am afraid of him. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. nonnorm_leg4
The main reason I would obey the police officer is because I am scared of getting in trouble. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. nonnorm_leg5
I would do what the police officer tells me because I fear how he would react if I didn’t. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. BSC1
I am good at resisting temptation. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4)
  1. BSC2R
I have a hard time breaking bad habits. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4)
  1. BSC3R
I am lazy. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4)
  1. BSC4R
I say inappropriate things. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4)
  1. BSC5R
I do certain things that are bad for me, if they are fun. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4)
  1. BSC6
I refuse things that are bad for me. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4)
  1. BSC7R
I wish I had more self-discipline. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4)
  1. BSC8
People would say that I have iron self-discipline. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4)
  1. BSC9R
Pleasure and fun sometimes keep me from getting work done. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4)
  1. BSC10R
I have trouble concentrating. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4)
  1. BSC11
I am able to work effectively toward long-term goals. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4)
  1. BSC12R
Sometimes I can’t stop myself from doing something, even if I know it is wrong. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4)
  1. BSC13R
I often act without thinking through all the alternatives. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4)
  1. closed_expect1
How would characterize the police officer’s behavior in the video.
  1. closed_expect2
Think back to the behavior you expected from the police. How much did the officer act as expected?
  1. rank_pj_1
With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer opens with a polite greeting
  1. rank_pj_2
With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer explains why they are interacting with the citizen
  1. rank_pj_3
With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer calls the citizen by an appropriate title/name
  1. rank_pj_4
With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer thanks the citizen for your cooperation
  1. rank_pj_5
With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer explains how to proceed in the legal process
  1. rank_pj_6
With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer explains the consequences of non-compliance
  1. rank_pj_7
With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer says goodbye to the citizen in a polite manner
  1. rank_pj_8
With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer asked the citizen to provide information/viewpoint
  1. rank_pj_9
With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer indicated he would not make a decision about what to do until s/he had gathered all the necessary information
  1. rank_pj_10
With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer explains the policy regarding their actions
  1. rank_pj_11
With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer explained why s/he chose to resolve the situation as s/he did
  1. rank_pj_12
With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer offered comfort or reassurance to this citizen
  1. rank_pj_13
With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer provided or promised to provide advice handling the situation/problem
  1. rank_pj_14
With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer indicates how citizen can file a complaint
  1. memo
Please specify which behaviors listed below you recall the officer exhibiting during the video you watched.
  1. over_fair
Overall, how fair or unfair was the interaction?
  1. Male
What is your Gender? (Male = 1, Female = 0, Other = 0)
  1. Male_other
If you answered ‘other’ to Gender, please specify.
  1. Male_split
Gender was dichotomized (Male = 1, All else = 0)
  1. ethnicity
Are you of Hispanic, Latino, or Spanish origin? (No = 0, Yes, Mexican, Mexican American, Chicano = 1, Yes, Puerto Rican = 2, Yes, Cuban = 3, Yes, other Hispanic, Latino, or Spanish origin = 4)
  1. race
What is your race? (select all that apply) (White = 0, Black or African American = 1, American Indian or Alaska Native = 2, Asian = 3, Middle Eastern = 4, Pacific Islander = 5, Other = 6)
  1. race_other
If you answered ‘other’ to race, please specify
  1. race_split
Race was dichotomized (White = 0, All else = 1)
  1. income
What is the yearly household income level? (Less than $34,999 = 0, $35k-$49,999 = 1, $50,000-$74,999 = 2, $75k,000-$99,999 = 3, $100k or more = 4)
  1. educ
What is the highest level of education you reached? (Less than high school = 0, High school or equivalent diploma, some college, or associate’s degree = 1, Bachelor’s degree = 2, Master’s, professional, or doctoral degree = 3)
  1. occup
How would you classify your current occupation in the scale below? (Unemployed = 0, Unskilled manual labor = 1, Skilled manual labor = 2, Professional labor = 3)
  1. married
What is your marital status? (Never married = 0, Not married, but in long term relationship = 1, Married = 2, Divorced = 3, Widowed = 4)
  1. region
Identify the area of the country where you currently live. (Northeast = 0, Midwest = 1, West = 2, South = 3)
  1. region_split
Region was dichotomized (South = 1, Northeast, Midwest, & West = 0)
  1. community
Select the option that best describes the community where you live. (Urban = 0, Suburban = 1, Rural = 2)
  1. community_split
Community type was dichotomized (Rural = 1, Urban & Suburban = 0)
  1. pol_orient
Generally speaking, do you consider yourself a part of one of the political parties listed below? (Democrat = 0, Republican = 1, Independent = 2, Socialist = 3, Libertarian = 4, Something else = 5, I do not identify with any political party = 6)
  1. pol_scale
Where would you place yourself on the following scale. (Very conservative = 0, Conservative = 1, Slightly conservative = 2, Centrist = 3, Slightly Liberal = 4, Liberal = 5, Very Liberal = 6)
  1. homeown
Are you a homeowner or renter? (Renter = 0, Homeowner = 1)
  1. homeown_length
How long have you lived in your current home?
  1. citizen
Are you a citizen of the United States? (No = 0, Yes = 1)
  1. fluency
Are you fluent in English?
  1. pol_fam
Is there anyone close to you who is a police officer (i.e., family, friends, intimate partner)? (No = 0, Yes = 1)
  1. pol_contact
Have you had any personal contact with the police in the past 12 months?
  1. pol_encounters
Please estimate how many encounters have you had with the police in your lifetime?
  1. pol_type
Please think about that contact or if there was more than one contact, the most recent one. Which of the following best describes your contact with the police? (I called the police to report a crime = 0, I called the police to report an accident = 1, I called the police to request information = 2, I was pulled over by the police while I was driving = 3, Something else = 4)
  1. arrested
Have you ever been arrested before? (No = 0, Yes = 1)
  1. arrested_time
If ‘Yes’, how long ago was your (last) arrest?
  1. victim_1
Using the scale provided below, please tell us whether you have been a victim of any of the following crimes in the past year. - Assault (an unlawful attack by one person upon another for the purpose of inflicting injury).
  1. victim_2
Using the scale provided below, please tell us whether you have been a victim of any of the following crimes in the past year. - Burglary (the unlawful entry of a structure to commit a theft).
  1. victim_3
Using the scale provided below, please tell us whether you have been a victim of any of the following crimes in the past year. - Theft (the unlawful taking of property from the possession of another).
  1. victim_4
Using the scale provided below, please tell us whether you have been a victim of any of the following crimes in the past year. - Vandalism (the destruction or defacement of property without the consent of the owner).
  1. victim_5
Using the scale provided below, please tell us whether you have been a victim of any of the following crimes in the past year. - Internet crime (such as consumer fraud, identity theft, or virus)
  1. victim_6
Using the scale provided below, please tell us whether you have been a victim of any of the following crimes in the past year. - Other (Please specify)
  1. victim_other
If ‘Other’, please describe your victimization experience.
  1. check1
How honest were you in answering the questions? (Not at all honest = 0, A little honest = 1, Moderately honest = 2, Very honest = 3, Completely honest = 4)
  1. check2
When going through the survey, how carefully did you read the questions? (Not carefully at all = 0, Not very carefully = 1, Moderately careful = 2, Carefully = 3, Extremely carefully = 4)
  1. check3
Did the encounter you watched seem realistic? (Definitely not = 0, Probably not = 1, Might or might not = 2, Probably yes = 3, Definitely yes = 4)
  1. PROLIFIC_PID
PROLIFIC_PID
  1. age
Participant age
  1. SES
A combined factor for socio-economic status consisting of occupation, education, and income
  1. video_condition
Video conditions: 0 - Negative condition, 1 - Neutral condition, 2 - Positive condition
  1. dist_just
Distributive justice factor (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. pol_effect
Police effectiveness factor (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. legal_cyn
Legal cynicism factor (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. expected_pj
Expected PJ factor (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. global_pj
Global procedural justice factor (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. specific_pj
Specific procedural justice factor (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. SDS
Social Desirability scale factor (False = 0, True = 1)
  1. norm_leg
Normative legitimacy factor (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. nonnorm_leg
Non-normative legitimacy factor (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4)
  1. BSC
Brief self control factor (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4)
  1. diff1
specific_pj1 - expected_pj1
  1. diff2
specific_pj1 - expected_pj2
  1. diff3
specific_pj1 - expected_pj3
  1. diff4
specific_pj1 - expected_pj4
  1. diff5
specific_pj1 - expected_pj5
  1. diff6
specific_pj1 - expected_pj6
  1. diff_scores
Difference between Specific_PJ - expected_pj factor
  1. global_pj_cent
Global procedural justice mean centered
  1. expected_pj_cent
Expected procedural justice mean centered
  1. video_cond
Video conditions abbreviated
  1. expected_pj_c
Expected procedural justice mean centered and abbreviated
  1. global_pj_c
Global procedural justice mean centered and abbreviated
  1. video_condition_1
Neutral condition dummy coded
  1. video_condition_2
Positive condition dummy coded
  1. VC1
Video condition 1 abbreviated
  1. VC2
Video condition 2 abbreviated
  1. PE
Police effectiveness abbreviated
  1. DJ
Distributive justice abbreviated
  1. LC
Legal cynicism abbreviated
  1. GPJ
Global procedural justice abbreviated
  1. SPJ
Specific procedural justice abbreviated
  1. NL
Normative legitimacy abbreviated
  1. NNL
Non-normative legitimacy abbreviated
  1. ExPJ
Expected procedural justice abbreviated
  1. VC1_GPJ
Interaction term of Neutral condition x Global PJ
  1. VC2_GPJ
Interaction term of Positive condition x Global PJ
  1. arrest
Have you been arrested before abbreviated
  1. VC1_ExPJ
Interaction term of Neutral condition x Expected PJ
  1. VC2_ExPJ
Interaction term of Positive condition x Expected PJ